Visual tracking estimates the states of a moving target in a video. This technology is the most important and fundamental topic in computer vision and has several applications

such as surveillance

vehicle tracking

robotics

and human-computer interaction. L1 object-tracking based on sparse representation expresses each target candidate as a linear combination of dictionary templates. In such tracking

the global information is considered without analyzing the local information. To overcome drifting problems in background clutter

this paper proposes a tracking method based on positive patch voting. Given the over completeness of sparse representation dictionary and sensitivity to changing local features

we present the target by a set of image patch particles to consider the local structure of target templates. Extracting image patches is the core of our algorithm and directly affects the result of tracking. Specifically

we present a tracking reliability metric to measure how positively a patch can be tracked. Accordingly

a probability model is proposed to estimate the distribution of positive patches under a sequential Monte Carlo framework. To estimate how likely a patch can be preliminarily obtained

we adopt the peak-to-sidelobe ratio as a confidence metric. This ratio is widely used in signal processing to measure the signal peak strength in response map. The confidence function is proportional to the response map of image patches

and is a distance function between the templates and patches. Instead of using computationally intensive unsupervised clustering methods to group the image patches

we simply divide the image into two regions by a rectangle box that is obtained by the L1 method centering at the target. We then formulate a similarity function to measure the patches that are inside the bounding box and a confidence score that is higher than zero. We label the patches that obtained the high score. Finally

we calculate the weight of all positive patches and vote the optimal location of the tracked target. The traditional target tracking based on sparse representation simply considers global information without analyzing the local information

and L1 object tracking easily produces drifting problems under complex situations. Thus

we present the target by a set of image patches and formulate a new patch reliability metric to extract the positive patches. Qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed algorithm can handle highly diverse and challenging situations of visual tracking. Sparse representation is applied to visual tracker by modeling the target appearance using a sparse approximation over a template set. However

the proposed method cannot adapt to complex and dynamic scenes because of various factors

such as background clutter and illumination change. By formulating the confidence and similarity function

we extract the positive patches. We finally find the target location according to voting of positive patch weight. Unlike other classical methods

our method can deal with occlusion

illumination change

and fast movement to accomplish robust tracking in sequence with background clutters.