Authors:
Ala Mhalla
1
;
Thierry Chateau
2
;
Sami Gazzah
3
and
Najoua Essoukri Ben Amara
3
Affiliations:
1
LATIS ENISo, University of Sousse, Institut Pascal and Blaise Pascal University, Tunisia
;
2
Institut Pascal and Blaise Pascal University, France
;
3
LATIS ENISo and University of Sousse, Tunisia
Keyword(s):
Transfer Learning, Deep Learning, Faster R-CNN, Sequential Monte Carlo Filter (SMC), Pedestrian Detection.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Camera Networks and Vision
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Image Formation and Preprocessing
;
Image Formation, Acquisition Devices and Sensors
;
Motion, Tracking and Stereo Vision
;
Video Surveillance and Event Detection
;
Visual Attention and Image Saliency
Abstract:
The performance of a generic pedestrian detector decreases significantly when it is applied to a specific scene due to the large variation between the source dataset used to train the generic detector and samples in the target scene. In this paper, we suggest a new approach to automatically specialize a scene-specific pedestrian detector starting with a generic detector in video surveillance without further manually labeling any samples under a novel transfer learning framework. The main idea is to consider a deep detector as a function that generates realizations from the probability distribution of the pedestrian to be detected in the target. Our contribution is to approximate this target probability distribution with a set of samples and an associated specialized deep detector estimated in a sequential Monte Carlo filter framework. The effectiveness of the proposed framework is demonstrated through experiments on two public surveillance datasets. Compared with a generic pedestrian
detector and the state-of-the-art methods, our proposed framework presents encouraging results.
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