Integration of colour and uniform interlaced derivative patterns for object tracking
Seyedeh Fatemeh Razavi, Hedieh Sajedi, Mohammad Ebrahim Shiri · IET Image Processing · 2016
Object tracking as a branch of computer vision plays a key role in the intelligent video surveillance. In recent years, the mean shift algorithm due to its simplicity and robustness has attracted great attention for tracking the object by using a colour model, while only using the colour causes the error in some cases of tracking such as illumination variations and so on. Consequently, the authors proposed an enhanced mean shift tracking algorithm. First, they presented a new texture‐based target representation by a modified version of the interlaced derivative pattern, which considers spatial dependencies between pixels. Second, an improvement for the mean shift tracking algorithm based on this representation is suggested. In addition, a parameter to resize the window around the object is considered, adaptively. Experimental results on some benchmark video in comparison with other state‐of‐art methods show the efficiency and utility of the proposed algorithm in many complex conditions.