Voting-based simultaneous tracking of multiple video objects

This paper proposes an automatic object tracking method based on both object segmentation and motion estimation for real-time content-oriented video applications. The method focuses on the issues of speed of execution and reliability in the presence of noise, coding artifacts, shadows, occlusion, and object split. Objects are tracked based on the similarity of their features in successive frames. This is done in three steps: feature extraction, object matching, and feature monitoring. In the first step, objects are segmented and their spatial and temporal features are computed. In the second step, using a nonlinear two-stage voting strategy, each object of the previous frame is matched with an object of the current frame creating a unique correspondence. In the third step, object changes, such objects occlusion or split, are monitored and object features are corrected. These new features are then used to update results of previous steps creating module interaction. The contributions in this paper are the real-time two-stage voting strategy, the monitoring of object changes to handle occlusion and object split, and the spatiotemporal adaptation of the tracking parameters. Experiments on indoor and outdoor video shots containing over 6000 frames, including deformable objects, multi-object occlusion, noise, and coding and object segmentation artifacts have demonstrated the reliability and real-time response of the proposed method.