Video Surveillance Classification-based Multiple Instance Object Retrieval: Evaluation and Dataset

In this paper we propose a classification-based automated surveillance system for multiple-instance object retrieval task, and its main purpose, to track of a list of persons in several video sources, using only few training frames. We discuss the perspective of designing appropriate motion detectors, feature extraction and classification techniques that would enable to attain high categorization accuracy, and low percentage of false negatives. Evaluation is carried out on a new proposed dataset, namely Scouter dataset, which contains approximately 36,000 annotated frames. The proposed dataset contains 10 video sources, with variable lighting conditions and different levels of difficulty. The video database raises several challenges such as noise, low quality image or blurring, increasing the difficulty of its analysis. Also, the contribution of this paper is in the experimental part, several valuable interesting findings are reported that motivate further research on automated surveillance algorithms. The combination and calibration of appropriate motion detectors, feature extractors and classifiers allows to obtain high recall performance.