Improved mean shift for multi-target tracking

Object tracking is critical to visual surveillance and activity analysis. The color based mean shift has been addressed as an effective and fast algorithm for tracking. But it fails in case of objects with low color intensity, clutter in background and total occlusion for several frames. We present a new scheme based on multiple feature integration for visual tracking. The proposed method integrates the color, texture and edge features of the target to construct the target model and a fragmented mean shift to handle occlusion. For further improvement target center is updated with Kalman filter and target model is also updated. The overall frame work is computationally simple. The proposed approach has been compared with other trackers using challenging videos and has been found to be performing better.