Target Recognition Approach for Efficient Sensing in Wireless

Multimedia Sensor Networks

Manal Alsabhan and Adel Soudani

College of computer and information systems, Department of Computer Sciences, King Saud University, Riyadh, KSA

Keywords WMSN, Multimedia Sensing, Object Recognition, Low-power, Fourier Descriptors, Zernike Moments

Abstract In Wireless Multimedia Sensor Networks (WMSN), image-based sensing applications face the issue of

energy efficiency and the availability of resources. This issue leads to image sensing and transmission

severely exhausting the sensor energy, potentially flooding the network with unnecessary data at the

application level. Compression of the image fails to solve this issue efficiently, due to the complexities of

the algorithm. Thus, the approach of employing image sensing to detect an event of interest locally prior to

transmission of the Region of Interest (ROI) would avoid useless data transmission, and consequently save

energy. This approach promises to extend the life of the entire network while reducing the sensing time. The

main contribution of this work is to establish a low-complexity scheme for image sensing in WMSN. This

scheme based on 2D General Fourier shape descriptors for target recognition and notification to the end

user. This current paper outlines the specification of the proposed scheme and its implementation on

wireless multimedia sensors. It addresses the performances evaluation regarding time and energy

consumption. The results reveal the high levels of accuracy of the proposed approach in efficiently

recognizing the target and notifying the end user. It shows a significant performance that overcomes the

efficiency of alternative similar sensing approaches that have been proposed in the literature.

1 INTRODUCTION

Wireless Sensor Networks (WSNs) have a

number of limitations related to processing, storage,

and communication capabilities, which severely

restrict their adequacy for multimedia sensing and

communication. In the majority of the new

applications of IoT and advanced communication

systems, the low cost of WSNs leads to them being

viewed as an attractive technology. The deployment

of WMSN is very attractive in the context of remote

image-based target recognition and tracking

applications. However, issues of power consumption

are of fundamental concern in these systems, leading

to questions concerning their efficiency and raising

the need to design new low-power approaches to

deliver multimedia content with a certain level of

quality of service to the end user.

The literature has confirmed that, due to the

complexities of these algorithms, image

compression techniques employed in WMSN form

inefficient approaches for low-power sensing (Chefi

A, 2014). One attractive method of minimizing

power consumption and extending the lifetime of a

network is to apply a local event-based sensing and

detection scheme. One way to accomplish this

method is the use of an ROI descriptors, firstly, to

detect, locally, whether the image captures

phenomena of interest and secondly, to send the

minimum required data to the end user. This

approach reduces the transmitted data to the sink

node. Consequently, it preserves the energy of the

source sensor, along with that of the complete

network. This method is based on notifying the end

user solely when an interesting phenomenon has

been detected in the observation scenes. It requires

the detection of the presence of a new object,

recognizing the target and notifying the end user

with the minimum number of bytes to increase the

network lifetime. This requires the design of a low-

complexity approach for image-based target

recognition, in order to implement it in an efficient

manner in the context of WMSN. A potential

candidate, for this objective, is the application of

shape descriptors (i.e., General Fourier descriptors)

for their low levels of complexity and accuracy in

the recognition process (Yang et al., 2008)

Alsabhan, M. and Soudani, A.

Target Recognition Approach for Efﬁcient Sensing in Wireless Multimedia Sensor Networks.

DOI: 10.5220/0006603900910098

In Proceedings of the 7th International Conference on Sensor Networks (SENSORNETS 2018), pages 91-98

ISBN: 978-989-758-284-4

The main contribution of this paper is to present a

promising new multimedia sensing-scheme, based

on the concept of event detection, employing 2D

General Fourier Descriptors as the feature’s

extraction method. The novelty of this scheme is its

ability to reduce communication overheads and per-

node power consumption, while also ensuring

efficient notification to the end user. Furthermore, it

addresses the performance of this scheme in relation

to efficient detection and low-energy processing.

This paper presents comparisons with related

solutions and demonstrates the power of the

proposed concept.

2 RELATED WORK

There are a number of contributions to the literature

addressing the design of efficient image-based

techniques for object recognition. However, these

primarily addressed computer-based applications,

and, as a result of design limitations, cannot be

adapted directly in sensors. Yang et al. (2008)

classified a number of approaches to the extraction

and representation of shape-based features according

to their processing methods. They presented a

number of different functions, i.e., a one-

dimensional function for shape representation;

polygonal approximation; a spatial interrelation

feature; moments; scale-space approaches; and

shape transform domains. This survey concluded

GFD techniques to be highly efficient and accurate

for object recognition.

Further research has attested to the efficiency of

employing GFD for feature extraction and

enhancing accuracy (Teague, 1980; AlSabhan,

2016). Zhang (2002) considered GFD an effective

detection method. However, he did not focus on

object detection and recognition in the context of

WMSN. Belongie et al. (2002), however, presented

a simple and accurate scheme for object matching in

the context of WMSN, based on the distance

between shapes, while Vasuhi et al. (2012)

employed the Haar wavelet for object feature

extraction. However, both works failed to address

the issue of power consumption. Zuo et al. (2012)

outlined a distributed two-hop clustered image

transmitting scheme, consisting of a trade-off

between computation and processing load, reflected

in enhancing the lifetime of the network. Irgan

(2014) employed hardware platforms for power

conservation (which have a high level of estimated

implementation costs) and made no consideration of

a scalable solution. Nikolakopoulos et al. (2013)

outlined a new scheme based on quadtree

decomposition for image compression, suggesting it

as an efficient solution for a low-power solution in

WMSN. Wang et al. (2008) outlined an artificial

immune system-based image pattern recognition.

This, however, contains very high levels of

associated energy consumption, rendering it

unsuitable for low-power processing.

3 IMAGE-BASED SENSING

SCHEME FOR TARGET

RECOGNITION

In the proposed approach, the sensor should be

capable of locally detecting the appearance of a new

object within the camera scene. It needs to decide

locally if the detected object is with interest to the

end user, followed by notifying the end user with a

few-bytes. The proposed concept is intended to

avoid transmitting multiple-images with the

potential to exhaust the local battery and load the

network with an unnecessarily high volume of data

as a result of the characteristics of multimedia

applications.

Figure 1: Structure of the image-based sensing scheme

based on GFD.

In order to achieve an efficient sensing scheme, the

design of the new approach focuses on addressing

the limitations of low-cost sensors, by ensuring: (1)

low computational complexity; (2) a low level of

required memory storage; and (3) an avoidance of

congesting the network bandwidth by limiting the

per-node communication overhead. These goals are

accomplished by allowing the end-user to remotely

configure the smart multimedia sensors by means of

target shape descriptors. Each sensor, subsequently,

begins to sense the surrounding environment in a

periodic manner. Once an event is detected, the

sensor commences the object detection and

extraction process internally from the captured scene

and applies feature extraction methods to obtain a

set of features vectors to match with a previously

memory loaded set. A notification is submitted to

the end user when the matching indicates significant

similarities. Otherwise, the sensed event is

SENSORNETS 2018 - 7th International Conference on Sensor Networks

discarded, and the sensor recommences the search

for an event. The structure of the proposed scheme

for object detection and recognition is described by

the sequential steps described in Figure1.

The proposed approach is a new scalable multimedia

sensing scheme capable of satisfying the constraints

of the limited energy and resources in WMSN. In

this approach, each multimedia sensor node

performs the sensing scheme for target detection and

recognition in an autonomous manner. It decides

whether the object appearing in the camera scene is

of interest to the end user, based on local

recognition-processing tasks. The basic assumption

of this approach is the reduction of node

communication activities through the radio link

while increasing local processing activities. Thus,

the capacity of this scheme to extend the per-node

lifetime depends on the complexity of the different

tasks involved during the processing cycle of the

detected object. The structure of proposed scheme

consists of the sequential steps as outlined below.

3.1 Detection of New Object

The detection of a new object is based on the

approach of background subtraction. It divides the

image into a set of blocks of eight by eight pixels.

The difference between the new image and the

background image is calculated at the pixel level

intensity changes, in order to detect a new object. If

the foreground block is noted by β

(j) and

background β

n-1

(j) respectively, then when the whole

difference through all the image blocks is greater

than a certain threshold (T

ther

), a new object should

be detected, as expressed in (1) (Lu, 2012).

(1)

3.2 Extraction of the Region of Interest

The set of blocks representing the ROI of useful

information in the image will be isolated to reduce

the processing load. Once the object is detected, the

scheme will extract set of blocks that form the useful

area and isolate it from unnecessary blocks. These

blocks will subsequently be transformed to binary

level for further processing to identify the shape (see

Figure 2). This step reduces both the memory

occupancy and energy consumption related to pixel

processing.

Figure 2: Binarizing useful extracted blocks.

3.3 Extraction of Feature’s Vectors

Our presented approach for extracting a set of

signature features vector is based on General Fourier

Descriptor (GFD). It is one of shape classification

and description approach, employed in a large

number of application fields based on image

processing, due to: (1) ease of computation; (2) low

complexity; (3) robustness to noise; and (4)

compactness. From a mathematical perspective, a

comparison with Zernike Moments (ZM) (i.e., a

well-known technique for describing shapes (Zhang

et al., 2002)) reveals that GFD has no redundant

features because there is no repetition and it permits

examination of features in both radial and angular

directions. These are also compared in the

implementation section below from the perspective

of in-node power consumption

GFD is primarily deployed to transform the shape

signature using Fourier transformation based on

signature region. Firstly, GFD transforms the input

image f(xi,yi) of size N*M where f defined by

{f(xi,yi): 1≤ i ≤ M, 1 ≤ j ≤ N} into a polar image

f(r,ө) using the following equations:

(2)

(3)

where and are the mass center of the shape.

They are calculated by the following equations:

(4)

(5)

Secondly, the Fourier transformation takes place in

order to extract the signature feature vector set,

Target Recognition Approach for Efﬁcient Sensing in Wireless Multimedia Sensor Networks

referred to as Fourier Descriptors (FD), using the

following equation:

(6)

Where ρ and φ reflect the image size,

, and

The FD is translation invariant. However, in order

to achieve the rotation and scaling invariant, a

normalization step should be applied to the extracted

feature vector set, as in the following equation:

(7)

where m is the maximum number of radius

frequencies, and n is the maximum number of

angular frequencies. To establish an efficient

signature description, Zhang et al. (2002)

recommended thirty-six GFD features, reflecting

four radial frequencies and nine angular frequencies.

The most important step is that of the extracted

shape vectors, which play a significant role in the

overall performance of the identification scheme. An

examination of the image based object identification

approach reveals a need to focus on how they

address the important challenges of WMSN, the

most significant of which are as follows.

3.3.1 Low Sensing Power Consumption

The processor consumes a considerable proportion

of power from the overall sensor resident battery. In

computational theory, there is a proportional

relationship between the total number of processor

clock cycles and the arithmetic and logic operations

undertaken in a microprocessor. This is also true of

the amount of power consumed.

3.3.2 Preserve Memory Capacity

Sensor nodes are equipped with a low memory

capacity, to ensure the memory size is not exhausted

by the extraction of the signature information. The

memory in the current scheme is preserved through

the set extracted signatures features being expressed

in a limited size (i.e., one feature vector requires

eight bytes, ensuring that thirty-six feature vectors

are required for 288 bytes).

3.3.3 Communication Overhead

The limited size of the extracted feature vector set

promises few communication overheads, while also

minimizing traffic over the bandwidth in comparison

to sending whole image bytes or employing the

compression method. An efficient sensing scheme

for object identification in WMSN is essential for

the consideration of limitations and challenges.

There are a large number of effective shape

descriptor approaches employed in the image

processing field for object identification and

classification, a minor set is also present (due to the

constraints of the sensor node’s design) capable of

being adapted in WMSN. These constraints are

divided into two factors: (1) low process capability

and (2) limited memory capacity. Further

approaches can be adapted for use in the context of

WMSN, but these require definite modifications in

node design, which raise the cost of implementation,

resulting in imperfect scalability.

3.4 Target identification

The Mean Square Errors (MSE) was employed to

identify whether the isolated signature forms a

promising target, in order to estimate the similarity

between the remotely loaded reference descriptors

within the local memory.

(8)

Where n represents the total vector set, x

denotes

the i

feature vector of the extracted signature and

denotes the i

feature vector of the reference. The

features vector set obtained from the previous step

was employed to establish the similarity with the

remotely loaded reference features vector set. If the

difference is less than certain threshold T

Difference

, the

detected object is declared as the target, and the

sensor notifies the user. Otherwise, the detected

object does not represent useful information to the

end user, and the image is therefore ignored.

3.5 End User Notification

Finally, when the sensor identifies the target, it

notifies the end user. Notification is undertaken

according to the requirements of remote user

SENSORNETS 2018 - 7th International Conference on Sensor Networks

applications. Thus, this step represents the potential

for achieving a considerable saving in time and

power-consumption by sending a single byte

message, a set of feature vectors or useful extracted

blocks from the image. On-demand notification

requests by the end user relieve the bandwidth from

congestion by minimizing both the volume of

transmitted data and the need for retransmitting in

case of error. The approach leads to unheavy traffic

load, thus prolonging the life of the entire network.

4 IMPLEMENTATION AND

PERFORMANCES ANALYSIS

4.1 Target Recognition Capabilities

The scheme based on the GFD algorithm using

Matlab was implemented to establish the efficiency

of the selected shape descriptor and demonstrate that

it satisfies the requirements of accurate recognition.

A set of images (AlSabhan, 2016) was employed to

evaluate the capability of the scheme in target

recognition to recognize an object under different

invariance conditions, i.e., rotation, scale, and

translation. The images employed were those in the

experiment of 64*64 and 128*128 grayscale eight

bpp. Figure 3 demonstrates the test image set, which

was composed of twelve images.

Figure 3: Testing images set: (1) original reference; (2)

Translated object to up; (3) to corner; (4) to down; (5)

Rotated object by 30°; (6) by 55°; (7) by 65°; (8) by 90°;

(9) maximize object by 55%; (10) maximize by 65%; (11)

minimize by 25%; and (12) minimize by 35%.

Based on the recommendations of Zhang et al.

(2002), the GFD radiance frequencies were set in

equation (6) to three, and angular frequencies to

eight, thus giving a total set of twenty-four different

features vectors. The features of these objects were

employed to calculate the similarity with the

reference target-image features vectors, using MSE

to identify the target. Figures 4 and 5 illustrate the

extracted features for all images in Figure 3 using

GFD and ZM (AlSabhan, 2016) techniques,

respectively.

Figure 4: Extracted vector set using GFD.

Figure 5: Extracted vector set using ZM.

In figures 4 and 5, curves represent by how much the

tested images are close to the reference, by

measuring the deviation of the signature features’

vectors from the reference features vector set using

MSE. These curves can be seen as being almost

identical using GFD or ZM, but GFD reveals more

accurate results with a neglected difference in

comparison to ZM for scaled objects, with a very

low difference, i.e., less than 1. This resulted from

the nature of the ZM computational algorithm,

which is insensitive to rotation only and needs to

normalize the object to a predefined scale ratio.

However, the use of a pre-processor step to establish

the object in normalization form overrides this

difference but increases the complexity of the

algorithm. ZM needs to consider the re-center of the

Target Recognition Approach for Efﬁcient Sensing in Wireless Multimedia Sensor Networks

object and to return it to a predetermined scale size.

This pre-processing step is not mandatory in GFD,

due to the normalization equation (7) that drives the

GFD ability to be invariant to all object translation,

scaling and rotation, thus resulting in lower

processing complexity and energy consumption than

in ZM. Table 1 provides a summary of the capacity

of the proposed scheme to recognize the target

employing GFD and ZM methods. This reveals a

high accuracy in acknowledging that the target with

MSE almost equals zero under different positions,

including translation, rotation, and scaling. In ZM,

the scaled image loses some of its details and ZM is

highly sensitive to such differences. A comparison

between GFD and ZM results revealed that GFD

feature vectors contain less deviation from the

reference vector set in comparison to the ZM results,

and in particular for scaled images.

Table 1: MSE between features of the target and the new

object extracted by GFD and ZM techniques.

Mean Square Error (MSE)

Image Number

GFD

0.0001

5.410-06

0.0001

3.00E-06

0.0002

9.10E-07

0.0001

2.50E-07

0.00001

6.00E-07

0.0002

1.10E-05

0.0001

1.80E-06

0.0004

1.00E-05

0.0007

1.00E-06

0.0045

1.40E-07

0.0004

1.00E-05

0.0048

5.10E-07

These results establish that the proposed scheme

provides a robust and accurate method of shape

descriptors to recognize a specific target. It also

establishes a high capacity for capturing significant

features of the sensed object while minimizing the

complexity of the algorithm by its invariant

characteristics, leading to the potential for lower

power consumption.

4.2 Energy Consumption Evaluation

The efficiency of the proposed image-based

sensing scheme for low-power target recognition

should be evaluated in relation to its capability to

save energy in the camera node, consequently

extending the lifetime of the network, as well as

evaluating the memory occupancy in the node. The

proposed scheme generates a set of twenty-four

different features vectors, extracted from the image

of the detected object. Each of these vectors requires

four bytes to be represented in the memory, leading

to a total of ninety-six bytes from the memory

storage capacity. In comparison to the features

extracted with ZM (AlSabhan, 2016), a hundred

bytes are required in the memory to store the set of

twenty-five features of four bytes. Table 2

summarizes the storage required for both detection

and recognition schemes.

Table 2: Memory storage for sensing scheme based on

GFD and ZM

Size of

GFD

Stored program text

10 KB

3 KB

Stored background image

(64*64 of 8bpp)

4 KB

Captured image (64*64 of

8bpp)

4 KB

Stored 24 reference

descriptors (float-number)

100 bytes

96 bytes

Region of interest in binary

512 bytes

Extracted ROI descriptors

100 bytes

96 bytes

Total Approx.

19 KB

12 KB

This scheme can save up to 7KB of memory storage,

while (due to the equation complexity of ZM)

additional processing space is required in

comparison to GFD. Recent memory designs have

ensured that this algorithm can accomplish a

reasonable low level of storage. The performance of

the proposed scheme was estimated for

implementation in wireless sensors (MICA2 sensors)

in terms of energy and time. The focus was

primarily placed on the evaluation of the processing

related performances of the scheme using GFD. The

AVRORA simulator for the ATmega128L

microcontroller (MICA2) was employed to

implement the algorithm. This tool determined the

number of clock cycles for the Atmel series for the

different tasks (J. Palsberg et al., n.d. ). The power

consumption and processing time were subsequently

estimated using the characteristics of

microcontrollers for MICA2. Grey-scale images of

(128*128 pixels- 8bpp) and (64*64 pixels- 8bpp)

were employed to extract the feature’s vector of the

detected object with a size of ninety-six bytes as a

total for twenty-four different image feature

descriptors.

Tables 3 and 4 illustrate the time and energy

consumption of internal processing when

implemented in the Camera-equipped MICA2

sensor, using ATmega128L for both sensing

SENSORNETS 2018 - 7th International Conference on Sensor Networks

schemes. Table 4 demonstrates identical

measurements for a similar sensing scheme using

ZM in the features extraction (AlSabhan, 2016). This

reveals the consumed energy for 64*64 pixels of 8

bpp image is 2.59 mJ, and for 128*128 pixels of 8

bpp is 10.34 mJ, while in the sensing scheme using

ZM, the internal processing was 33.5 mJ and 121

mJ, respectively to 64*64 pixels of 8 bpp and

128*128 pixels of 8 bpp image sizes.

Table 3: Evaluation of sensing scheme based on GFD

features on MICA2.

64 ∗ 64 pixels

8 bpp

128∗128 pixels

8 bpp

Time

(s)

Energy

(mJ)

Time

(s)

Energy

(mJ)

Object Detection

and Extraction

0.1

2.25

0.39

9.06

Feature

Extraction using

GFD

0.015

0.34

0.06

1.37

Total scheme for

GFD without

Notification

0.12

2.59

0.45

10.43

Table 4: Evaluation of the sensing scheme based on ZM

features on MICA2 sensors.

64 ∗ 64 pixels

8 bpp

128∗128 pixels

8 bpp

Time

(s)

Energy

(mJ)

Time

(s)

Energy

(mJ)

Object Detection

and Extraction

0.47

1.9

Target

Normalization

0.06

1.5

0.26

Feature Extraction

using ZM

0.98

3.18

Total scheme for

ZM without

Notification

1.51

33.5

5.34

121

Thus, the sensing scheme employing GFD

features outperforms the scheme based on ZM, i.e.,

saving approximately 91% additional energy than

the scheme using ZM features. This difference in

power consumption is explained by the low-

complexity of the extraction method of GFD

features, which requires less pre-processing of the

image.

Table 5 establishes different notification scenarios

and their associated energy costs, assuming the use

of IEEE 802.15.4 and ZigBee communication

standards. The simulation program is developed

using NesC language and evaluated on AVRORA,

using the TinyOS platform. This table illustrates that

the cheapest manner of notification is to send a 1-

byte message, with costs increasing if the extracted

feature set is sent to the end user (which is helpful in

some applications for further classification), but

remaining less than sending the whole image or RoI.

The notification with the GFD features vector

requires the same energy of the transmission of the

ZM features. However, the accuracy of the features

extracted by the GFD method should be noted, in

addition to low energy consumption in the internal

processing.

Table 5: End user’s notification types using MICA2

sensors.

Notification Type

Time

(s)

Energy

(mJ)

Notification with 1 byte

0.01

0.3

25 ZM features vectors

1.0

24 GFD features vectors

0.96

28.8

ROI transmission

4.40

132

captured image

40.96

1228.8

4.2.1 Comparison with Related Works

In comparison to similar approaches for multimedia

sensing, this current scheme presents a number of

attractive characteristics in relation to both

complexity and power consumption. A summary

takes place below of the most relevant reported

solutions in the literature and their characteristics in

comparison to the current proposal efficient

solutions for low-power sensing.

A comparison of the current scheme with

compression techniques discussed in the literature

establishes that this scheme achieves very low

processing complexity and efficient power

consumption. Irgan (2014) based the compression

algorithm on the priorities of the segment blocks for

compression, but consumed around (130 mJ), where

that of Nikolakopoulos (2013) consumed

approximately (45 mJ) for sending (64*64 of 8 bpp)

of image size. The scheme of Zuo (2012) consumed

approximately (1.4 J) for sending (512*512 of 8

bpp) of image size. However, the presented

distributed processing fits within the context of the

current scheme, in order to prolong the network life

and it will be addressed as future work. Wang

(2008) presented an image recognition pattern

algorithm but failed to evaluate the performance

analysis of power consumption from local

processing. Pham (2013) presented a scheme for

extracting ROI based on a hardware solution, which

demonstrated high-performance levels in low-

Target Recognition Approach for Efﬁcient Sensing in Wireless Multimedia Sensor Networks

complexity, but proved inflexible and was not

considered as scalable.

Alhilal (2015) employed recognition methods based

on centroid distance curvature features. However,

these failed to prove highly accurate and contained a

wide sensitivity to the characteristics of the detected

object in the image. Alsabhan (2016) investigated

ZM where it outperforms in term of the accuracy of

detection and algorithm complexity.

As outlined in Section 4.2, this current design

presents a low-complexity scheme and high

detection and recognition capability using GFD. It is

therefore concluded that the current work

outperforms other solutions presented in the

literature for image detection and recognition in

WMSN, in term of accuracy, efficiency, and

scalability.

5 CONCLUSIONS

This paper has presented a new sensing approach

based on GFD, as a shape descriptor for target

recognition in a monitored environment using

WMSN. The presented scheme is intended to

prolong the life of a network by minimizing the

power consumed by both the internal processor and

the transmitter antenna. The paper introduced the

simulation results attesting to the robustness,

accuracy, and low levels of complexity for target

recognition in WMSN. It was concluded that, in

comparison to a scheme based on ZM, the current

scheme requires less memory space for processing.

However, the internal sensor processing saves 91%

of energy in comparison to the application of ZM. It

is suggested that future work could include an

investigation of the communication overheads in the

network, which would result in a clearer concept of

the efficiency of this solution for deployment in

WMSN. Prior to such research, this current scheme

will be upgraded to handle multiple target detection

for simultaneous monitoring. It is concluded that the

concept of distributed processing as an approach to

energy saving appears promising. However, the

design and the implementation of a generic

clustering and processing architecture is still a

subject of open research.

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