Efficient Multi-Target Tracking Based on Meanshift and PNN

2013 ◽  
Vol 380-384 ◽  
pp. 3946-3949
Author(s):  
Zhi Ming Wang
Keyword(s):  
Neural Network ◽  
Target Tracking ◽  
Video Analysis ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Background Model ◽  
Probability Neural Network ◽  
Multi Target Tracking

Multi-target tracking is one of the basic and difficult tasks in video analysis and understanding. This paper proposed an efficient tracking algorithm based on meanshift algorithm and PNN (Probability Neural Network) background model. Firstly, PNN detection results were used to initialize targets for meanshift tracking. Secondly, in the succeeding frames, every target was matched to detected regions before tracking. At last, only targets which couldnt match with new regions need tracking with meanshift tracking algorithm. Experimental results show that mean search steps for every target were dramatically reduced compare with original mean shift tracking algorithm.

Target Scale Adaptation Based on Corner Registration

2014 ◽  
Vol 556-562 ◽  
pp. 4260-4263
Author(s):  
Bing Yun Dai ◽  
Hui Zhao ◽  
Zheng Xi Kang
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Distance Ratio ◽  
Scale Adaptation ◽  
Mean Shift Algorithm

Target tracking algorithm mean-shift and kalman filter does well in tracking target. However, mean-shift algorithm may not do well in tracking the target which the size of target is changing gradually. Although some scholars put forward by 10% of the positive and negative incremental to scale adaptive,the algorithm can not be applied to track the target which gradually becomes bigger. In this paper, we propose registration corners of the target of the two adjacent frames, then calculate the distance ratio of registration corners.Use the distance ratio to determine the target becomes larger or smaller. The experimental results demonstrate that the proposed method performs better compared with the recent algorithms.

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.

Research on 3D multi-target tracking algorithm

2021 ◽  
Author(s):  
Tingting Kou ◽  
Hua Cai ◽  
Guangwen Liu ◽  
Yingchao Li
Keyword(s):  
Target Tracking ◽  
Tracking Algorithm ◽  
Multi Target Tracking
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