  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.
