In this paper, we tackle the problem of domain adaptation to perform object-classification and detection tasks in video surveillance starting by a generic trained detector. Precisely, we put forward a new transductive transfer learning framework based on a sequential Monte Carlo filter to specialize a generic classifier towards a specific scene. The proposed algorithm approximates iteratively the target distribution as a set of samples (selected from both source and target domains) which feed the learning step of a specialized classifier. The output classifier is applied to pedestrian detection into a traffic scene. We have demonstrated by many experiments, on the CUHK Square Dataset and the MIT Traffic Dataset, that the performance of the specialized classifier outperforms the generic classifier and that the suggested algorithm presents encouraging results.
Maamatou et al. REF put forward a transductive transfer learning method based on an SMC filter to iteratively build a new specialized dataset that was used to train a new specialized pedestrian detector.
Transductive Transfer Learning to Specialize a Generic Classifier Towards a Specific Scene
VISIGRAPP
Computer Science