Paper Info
Title
Specialization Of A Generic Pedestrian Detector To A Specific Traffic Scene By The Sequential Monte-Carlo Filter And The Faster R-Cnn
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.
Year
DOI
Venue
2017
10.5220/0006097900170023
PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISIGRAPP 2017), VOL 4
Keywords
Field
DocType
Transfer Learning, Deep Learning, Faster R-CNN, Sequential Monte Carlo Filter (SMC), Pedestrian Detection
Computer vision,Pedestrian,Computer science,Particle filter,Artificial intelligence,Traffic scene,Detector
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Ala Mhalla100.34
Thierry Chateau214918.92
Gazzah, S.3106.21
Najoua Essoukri Ben Amara420941.48