People tracking in dense crowds using optical flow clustering based superpixel

People tracking in crowded scenes should be focused since the problem is beneficial and challenging in computer vision field. However, the problem of "crowd tracking" is extremely difficult because hard occlusions, various motions and posture changes. Especially in the crowd tracking, we need to handle occlusion for more robust tracking. This paper tackles a robust crowd tracking based on combination of superpixel and optical flow tracking. The SLIC based superpixel algorithm adaptively estimates a boundary between person and background, therefore the combination of superpixel and optical flow tracking becomes a highly confident tracking for crowd tracking. The tracking experiments show significant results on the UCF crowd dataset in terms of performance rate with comparison.