A corpus for benchmarking of people detection algorithms
Pattern Recognition Letters
On collaborative people detection and tracking in complex scenarios
Image and Vision Computing
Collecting pedestrian trajectories
Neurocomputing
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The main contribution of this paper is a new people detection algorithm based on motion information. The algorithm builds a people motion model based on the Implicit Shape Model (ISM) Framework and the MoSIFT descriptor. We also propose a detection system that integrates appearance, motion and tracking information. Experimental results over sequences extracted from the TRECVID dataset show that our new people motion detector produces results comparable to the state of the art and that the proposed multimodal fusion system improves the obtained results combining the three information sources.