Online maximum a posteriori tracking of multiple objects using sequential trajectory prior

Yang, Min; Pei, Mingtao; Jia, Yingmin

doi:10.1016/j.imavis.2019.103867

Cited by 5 publications

(1 citation statement)

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“…In [21], MOT frameworks were comprehensively discussed based on their assessments, formulations, principles, drawbacks and scopes to be focused on in future examinations with quantitative comparisons. The authors in [22] addressed optimal object trajectory issues based on two maximum-aposteriori methods for MOT through object detection and data association measurements; however, these methods are not suitable for complex association probability models because of the association ambiguity caused by noise and frequent interobject communications. A multi-person tracking algorithm was designed based on object detection and data association strategies.…”

Section: Related Workmentioning

confidence: 99%

Efficient LiDAR-Trajectory Affinity Model for Autonomous Vehicle Orchestration

Mekala

Viriyasitavat

Park

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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Computation and memory resource management strategies are the backbone of continuous object tracking in intelligent vehicle orchestration. Multi-object tracking generates enormous measurements of targets and extended object positions using light detection and ranging (Lidar) sensors. Designing an adequate object-tracking system is a global challenge because of dynamic object detection and data association uncertainties during scene understanding. In this regard, we develop an intelligent multi-objective tracking (IMOT) system with a novel measurement model, called the box data association inflate (BDAI) model, to assess each target's object state and trajectory without noise by using the Bayesian approach. The box object filter method filters ambiguous detection responses during data association. The theoretical proof of the box object filter is derived based on binomial expansion. Prognosticating a lower-dimension object than the original point object reduces the computational complexity of vehicle orchestration. Two datasets (NuScenes dataset and our lab dataset) are considered during the simulations, and our approach measures the kinematic states adequately with reduced computation complexity compared to state-of-the-art methods. The simulation outcomes show that our proposed method is effective and works well to detect and track objects. The NuScenes dataset contains 28130 samples for training, 6019 examples for validation and 6008 samples for testing. IMOT achieves 58.09% tracking accuracy and 71% mAP with 5 ms pre-processing time. The Jetson Xavier NX consumes 49.63% GPU and 9.37% average power and exhibits 25.32 ms Manuscript

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Section: Related Workmentioning

confidence: 99%