Authors:
Tunç Alkanat
;
Emre Tunali
and
Sinan Öz
Affiliation:
ASELSAN Inc. Microelectronics, Turkey
Keyword(s):
Real-time Target Detection, Multiple Target Tracking, Temporal Consistency, Data Association, Target Probability Density Estimation, Adaptive Target Selection.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Motion, Tracking and Stereo Vision
;
Tracking and Visual Navigation
;
Visual Attention and Image Saliency
Abstract:
In this study, a real-time fully automatic detection and tracking method is introduced which is capable of handling variable number of targets. The procedure starts with multiple scale target hypothesis generation in
which the distinctive targets are revealed. To measure distinctiveness; first, the interested blobs are detected based on Canny edge detection with adaptive thresholding which is achieved by a feedback loop considering
the number of target hypotheses of the previous frame. Then, the irrelevant blobs are eliminated by two metrics, namely effective saliency and compactness. To handle the missing and noisy observations, temporal
consistency of each target hypothesis is evaluated and the outlier observations are eliminated. To merge data from multiple scales, a target likelihood map is generated by using kernel density estimation in which weights
of the observations are determined by temporal consistency and scale factor. Finally, significant targets are selected by an adapt
ive thresholding scheme; then the tracking is achieved by minimizing spatial distance
between the selected targets in consecutive frames.
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