Video object tracking (VOT) aims to determine the location of a target over a sequence of frames. The existing body of work has studied various image factors that affect VOT performance. For instance, factors such as occlusion, clutter, object shape, unstable speed and zooming, that
influence video quality, do affect tracking performance. Nonetheless, there is no clear distinction between scene-dependent challenges such as occlusion and clutter and the challenges imposed by traditional notions of “quality impairments” inherited from capture, compression, processing,
and transmission. In this study, we are concerned with the latter interpretation of quality as it affects video tracking performance. In this paper, we propose the design and implementation of a quality aware feature selection for VOT. First, we divided each frame of the video into patches
of the same size and extracted HOG, and natural scene statistics (NSS) features from these patches. Then, we degraded the videos synthetically with different levels of post-capture distortions such as MPEG-4, AWGN, salt and pepper, and blur. Finally, we defined the best set of features HOG
and NSS that generate the largest area under the curve in the success plots, yielding an improvement in the video tracker performance in videos affected by post-capture distortions.