Robust Target Tracking Algorithm Based on Superpixel Visual Attention Mechanism

2019 ◽  
Vol 10 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Jia Hu ◽  
Xiao Ping Fan ◽  
Shengzong Liu ◽  
Lirong Huang
Keyword(s):  
Visual Attention ◽  
Target Tracking ◽  
Attention Mechanism ◽  
Tracking Algorithm ◽  
Complex Environments ◽  
Superpixel Segmentation ◽  
Tracking Algorithms ◽  
Target Template ◽  
Comprehensive Test ◽  
Visual Attention Mechanism

As existing target tracking algorithms are prone to drift under complex environments, the authors propose a tracking algorithm based on superpixel segmentation and visual attention mechanism. The algorithm works on a particle filter framework, by conducting a superpixel segmentation first and then building a model of the visual attention mechanism for saliently mapping. After extracting HOG features of salient regions, the authors compared the HOG feature with the target template at last, in order to locate the target region in the new frame. The proposed algorithm is evaluated on a comprehensive test platform, in which the simulation show that our tracker is more efficacious and more efficient than the previous traditional target tracking algorithms to cope with target drift issue.

scholarly journals Target Adaptive Tracking Based on GOTURN Algorithm with Convolutional Neural Network and Data Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhengze Li ◽  
Jiancheng Xu
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Convolutional Neural Network ◽  
Target Tracking ◽  
Network Structure ◽  
Attention Mechanism ◽  
Tracking Algorithm ◽  
Adaptive Tracking ◽  
Target Template ◽  
Overall Performance

With the advent of the artificial intelligence era, target adaptive tracking technology has been rapidly developed in the fields of human-computer interaction, intelligent monitoring, and autonomous driving. Aiming at the problem of low tracking accuracy and poor robustness of the current Generic Object Tracking Using Regression Network (GOTURN) tracking algorithm, this paper takes the most popular convolutional neural network in the current target-tracking field as the basic network structure and proposes an improved GOTURN target-tracking algorithm based on residual attention mechanism and fusion of spatiotemporal context information for data fusion. The algorithm transmits the target template, prediction area, and search area to the network at the same time to extract the general feature map and predicts the location of the tracking target in the current frame through the fully connected layer. At the same time, the residual attention mechanism network is added to the target template network structure to enhance the feature expression ability of the network and improve the overall performance of the algorithm. A large number of experiments conducted on the current mainstream target-tracking test data set show that the tracking algorithm we proposed has significantly improved the overall performance of the original tracking algorithm.

scholarly journals Research on Target Tracking Algorithm Based on Siamese Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Haibo Pang ◽  
Qi Xuan ◽  
Meiqin Xie ◽  
Chengming Liu ◽  
Zhanbo Li
Keyword(s):  
Neural Network ◽  
Target Tracking ◽  
Evaluation Criteria ◽  
Attention Mechanism ◽  
Tracking Algorithm ◽  
Temporal Attention ◽  
Tracking Accuracy ◽  
Current Frame ◽  
Tracking Process ◽  
Recognition Ability

Target tracking is a significant topic in the field of computer vision. In this paper, the target tracking algorithm based on deep Siamese network is studied. Aiming at the situation that the tracking process is not robust, such as drift or miss the target, the tracking accuracy and robustness of the algorithm are improved by improving the feature extraction part and online update part. This paper adds SE-block and temporal attention mechanism (TAM) to the framework of Siamese neural network. SE-block can refine and extract features; different channels are given different weights according to their importance which can improve the discrimination of the network and the recognition ability of the tracker. Temporal attention mechanism can update the target state by adjusting the weights of samples at current frame and historical frame to solve the model drift caused by the existence of similar background. We use cross-entropy loss to distinguish the targets in different sequences so that their distance in the feature domains is longer and the features are easier to identify. We train and test the network on three benchmarks and compare with several state-of-the-art tracking methods. The experimental results demonstrate that the algorithm proposed is superior to other methods in tracking effect diagram and evaluation criteria. The proposed algorithm can solve the occlusion problem effectively while ensuring the real-time performance in the process of tracking.

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