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arXiv · 1706.10217

SMC Faster R-CNN: Toward a scene-specialized multi-object detector

Ala Mhalla·Thierry Chateau·Houda Maamatou·Sami Gazzah·Najoua Essoukri Ben Amara
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Abstract

Generally, the performance of a generic detector decreases significantly when it is tested on a specific scene due to the large variation between the source training dataset and the samples from the target scene. To solve this problem, we propose a new formalism of transfer learning based on the theory of a Sequential Monte Carlo (SMC) filter to automatically specialize a scene-specific Faster R-CNN detector. The suggested framework uses different strategies based on the SMC filter steps to approximate iteratively the target distribution as a set of samples in order to specialize the Faster R-CNN detector towards a target scene. Moreover, we put forward a likelihood function that combines spatio-temporal information extracted from the target video sequence and the confidence-score given by the output layer of the Faster R-CNN, to favor the selection of target samples associated with the right label. The effectiveness of the suggested framework is demonstrated through experiments on several public traffic datasets. Compared with the state-of-the-art specialization frameworks, the proposed framework presents encouraging results for both single and multi-traffic object detections.

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Ala Mhalla, Thierry Chateau, Houda Maamatou, Sami Gazzah, Najoua Essoukri Ben Amara. 2017-06-30. SMC Faster R-CNN: Toward a scene-specialized multi-object detector. https://doi.org/10.1016/j.cviu.2017.06.008

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