scholarly journals Multiple Object Detection and Tracking in Dynamic Environment using Real Time Video

2017 ◽  
Vol Volume-2 (Issue-1) ◽  
pp. 1090-1099
Author(s):  
Tanuja Kayarga ◽  
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Dynamic Environment ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
Multiple Object ◽  
Multiple Object Detection

scholarly journals Dynamic Multi-LiDAR Based Multiple Object Detection and Tracking

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1474 ◽  
Author(s):  
Muhammad Sualeh ◽  
Gon-Woo Kim
Keyword(s):  
Object Detection ◽  
Unscented Kalman Filter ◽  
State Of The Art ◽  
Autonomous Driving ◽  
Multiple Model ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
Multiple Object ◽  
Multiple Object Detection ◽  
Grid Based

Environmental perception plays an essential role in autonomous driving tasks and demands robustness in cluttered dynamic environments such as complex urban scenarios. In this paper, a robust Multiple Object Detection and Tracking (MODT) algorithm for a non-stationary base is presented, using multiple 3D LiDARs for perception. The merged LiDAR data is treated with an efficient MODT framework, considering the limitations of the vehicle-embedded computing environment. The ground classification is obtained through a grid-based method while considering a non-planar ground. Furthermore, unlike prior works, 3D grid-based clustering technique is developed to detect objects under elevated structures. The centroid measurements obtained from the object detection are tracked using Interactive Multiple Model-Unscented Kalman Filter-Joint Probabilistic Data Association Filter (IMM-UKF-JPDAF). IMM captures different motion patterns, UKF handles the nonlinearities of motion models, and JPDAF associates the measurements in the presence of clutter. The proposed algorithm is implemented on two slightly dissimilar platforms, giving real-time performance on embedded computers. The performance evaluation metrics by MOT16 and ground truths provided by KITTI Datasets are used for evaluations and comparison with the state-of-the-art. The experimentation on platforms and comparisons with state-of-the-art techniques suggest that the proposed framework is a feasible solution for MODT tasks.

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