Appearance and structural motion context for multi-target tracking in aerial video
Xiaolin Gu, Shilin Zhou, Lin Lei, Zhipeng Deng · 2017
For multi-object tracking in aerial videos which are acquired from moving cameras, the motion of cars are complicated by global camera movements and always unpredictable. To deal with such unexpected camera motion for online multi-vehicles tracking, structural motion context between objects has been used thanks to its robustness to camera motion. In this paper, we propose an effective data association method that exploits structural motion context in the presence of large camera motion. In addition, to further improve the robustness of algorithm against missing data due to the target being occluded behind other objects, an appearance context model is developed to represent appearance information of objects we need to track. The structural motion context and appearance context are then used to predict the location of the unobserved objects. Experimental results on VIVID datasets show the effectiveness of the proposed algorithm for multi-object tracking.