Abstract
To meet the challenge of video target tracking, based on a self-organization mapping network (SOM) and correlation filter, a long-term visual tracking algorithm is proposed. Objects in different videos or images often have completely different appearance, therefore, the self-organization mapping neural network with the characteristics of signal processing mechanism of human brain neurons is used to perform adaptive and unsupervised features learning. A reliable method of robust target tracking is proposed, based on multiple adaptive correlation filters with a memory function of target appearance at the same time. Filters in our method have different updating strategies and can carry out long-term tracking cooperatively. The first is the displacement filter, a kernelized correlation filter that combines contextual characteristics to precisely locate and track targets. Secondly, the scale filters are used to predict the changing scale of a target. Finally, the memory filter is used to maintain the appearance of the target in long-term memory and judge whether the target has failed to track. If the tracking fails, the incremental learning detector is used to recover the target tracking in the way of sliding window. Several experiments show that our method can effectively solve the tracking problems such as severe occlusion, target loss and scale change, and is superior to the state-of-the-art methods in the aspects of efficiency, accuracy and robustness.
Highlights
Object tracking has made remarkable progress in the past two decades [1,2,3], but due to the deformation of the target, sudden movement, light change, severe occlusion, out of field of vision and other factors leading to a large change in appearance, object tracking is still very challenging
The goal of the object tracking algorithm proposed in this paper is to use self-organization mapping neural network (SOM) and multiple correlation filters to deal with the following challenges in the visual tracking process: (1) the obvious changes in appearance over time; (2) changes in scale; (3) recover the goal from the tracking failure
We use the latest system of visual tracking evaluation standards to evaluate our methods, including overlap success rate (OS), distance precise rate (DP), OPE, temporal robustness evaluation (TRE) and spatial robustness evaluation (SRE)
Summary
Object tracking has made remarkable progress in the past two decades [1,2,3], but due to the deformation of the target, sudden movement, light change, severe occlusion, out of field of vision and other factors leading to a large change in appearance, object tracking is still very challenging. In order to cope with these changes, neural networks with memory function and correlation filters are widely used in object tracking. The existing tracking algorithms based on the neural network and adaptive model cannot maintain the long-time memory of the target appearance, and the updating of the model in the case of noise may lead to the drifting of the tracking target. Self-organization mapping neural networks and correlation filters attracted extensive attention in the field of image research and visual tracking [4,5,6]. The popularity of the self-organization mapping neural network (SOM) and associated filters is due to three important properties. The tracking algorithm based on SOM and correlation filter does not have the fuzziness problem of assigning positive and negative labels to sample data. Based on random sampling of the image area around the estimated target location, the existing detection-based tracking algorithm [4,11,12] trains the tracking classifier in an incremental manner
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