minimum variance quantization

Visual object tracking in arbitrary environments with arbitrary objects has gained considerable importance in recent years. A very significant feature, which makes a tracker useful, is real time tracking without needing GPU and pre-train algorithms. In the recent decade, the trackers, which function on the basis of discriminative correlation filters, have promised positive results in terms of both speed and accuracy. Although, in most of such methods, the estimation of the position of the object in each frame is computed based on transformation and pyramid scales, in Large Displacement Estimation of Similarity transformation algorithm, translation, scale and rotation are estimated in each frame. In this paper, the Histogram of Oriented Gradient is considered as feature extraction. Here, we adopt two different approaches. The first approach uses scaled images as a feature matrix by applying minimum variance quantization. The second approach, uses a combination of opposite color local binary patterns and Speeded-Up Robust Features. By using these two methods, we are able to extract helpful and fast features, and therefore improve the results of tracking against challenging attributes. The OTB-2015 dataset is utilized for evaluating tracker. The results show precision of trackers improve 3%. Additionally, the first tracker increase the result about 7% against low resolution and the second one can be helpful about 4% in facing rotation challenge.