Hierarchical-Based Object Detection with Improved Locality Sparse Coding

This paper proposes to extend the hierarchical method to be adapted to sequential frames, aiming at detecting the moving object in dynamic scenes. A novel two-layer model is proposed, in which dictionaries are learned through three different stages and the locality constrained sparse representation is improved. This leads more significant improvement for performance of both static image classification and moving object detection. The experimental results demonstrate that the proposed algorithm is efficient and robust compared with the state-of-the-art classification methods, and also able to detect moving object in the sequential frames accurately.