Video SAR Ground Moving Target Indication Based on Multi-Target Tracking Neural Network

2021 ◽  
Author(s):  
Wei Wang ◽  
Yao Hu ◽  
Zongyou Zou ◽  
Yuanyuan Zhou ◽  
Chen Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Target Tracking ◽  
Moving Target ◽  
Multi Target Tracking ◽  
Ground Moving Target Indication

Research on Moving Target Tracking Algorithm Based on Lidar and Visual Fusion

2018 ◽  
Vol 22 (5) ◽  
pp. 593-601
Author(s):  
Xiaoxiao Guo ◽  
Yuansheng Liu ◽  
Qixue Zhong ◽  
Mengna Chai ◽  
◽  
...  
Keyword(s):  
Target Tracking ◽  
Autonomous Vehicles ◽  
Joint Probability ◽  
Moving Target ◽  
Position Information ◽  
Moving Target Tracking ◽  
Decision Level ◽  
Multi Target Tracking ◽  
Single Sensor ◽  
Awareness System

Multi-sensor fusion and target tracking are two key technologies for the environmental awareness system of autonomous vehicles. In this paper, a moving target tracking method based on the fusion of Lidar and binocular camera is proposed. Firstly, the position information obtained by the two types of sensors is fused at decision level by using adaptive weighting algorithm, and then the Joint Probability Data Association (JPDA) algorithm is correlated with the result of fusion to achieve multi-target tracking. Tested at a curve in the campus and compared with the Extended Kalman Filter (EKF) algorithm, the experimental results show that this algorithm can effectively overcome the limitation of a single sensor and track more accurately.

scholarly journals Image-Based Multi-Target Tracking Through Multi-Bernoulli Filtering with Interactive Likelihoods

2017 ◽  
Author(s):  
Anthony Hoak ◽  
Henry Medeiros ◽  
Richard J. Povinelli
Keyword(s):  
Neural Network ◽  
Target Tracking ◽  
Deep Neural Network ◽  
Sequential Monte Carlo ◽  
Pedestrian Detection ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Tracking Accuracy ◽  
Multi Target Tracking ◽  
Bernoulli Filter

We develop an interactive likelihood (ILH) for sequential Monte-Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, AFL, and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (OSPA and CLEAR MOT). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.

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