... LNCS 13019 , pp . 16–28 , 2021 . https://doi.org/10.1007/978-3-030-88004-0_2 In recent years, with the launch of low-cost LiDAR, such. Check for 3D Multi-object Detection and Tracking with Sparse Stationary LiDAR 1 Introduction.

... multiple object tracking . In : CVPR , pp . 164–173 ( 2021 ) 13. Peng , J. , et al .: Chained - tracker : chaining paired attentive regression results for end - to - end joint multiple - object detection and tracking . In : Vedaldi , A ...

The 4-volume set LNCS 13019, 13020, 13021 and 13022 constitutes the refereed proceedings of the 4th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2021, held in Beijing, China, in October-November 2021.

This work investigates and improves the efficiency of deep learning for sparse 3D data to overcome the obstacles to the further development of this technology.

This work presents a solution for autonomous vehicles to detect arbitrary moving traffic participants and to precisely determine the motion of the vehicle.

Recent years have seen considerable progress in automotive safety and autonomous navigation applications, fueled by the remarkable advance of individual Computer Vision components, such as object detection, tracking, stereo and visual ...

This book shows how machine learning can detect moving objects in a digital video stream.

But, these algorithms are computationally slow when applied in real-time. This book presents and summarizes the various visual tracking algorithms and challenges in the domain.

This work proposes a feature-based probabilistic data association and tracking approach (FBPDATA) for multi-object tracking.

The developed signal-processing algorithm is supplemented with a convenient user interface for setting and running and inspecting the results during and after the data collection.It was concluded that the TScan performance is sufficient for ...