High-Precision Motion Detection and Tracking Based on Point Cloud Registration and Radius Search

Kalman-Based Scene Flow Estimation for Point Cloud Densification and 3D Object Detection in Dynamic Scenes

“…This method demonstrates significant performance improvements in various LiDAR-based detectors [44]. Motion detection methods based on point cloud registration, combining geometric and structural features with neural network-based interests, have enhanced the detection and tracking of moving objects [24]. Novel multi-task models have been introduced for simultaneous scene flow estimation and object detection, achieving significant improvements in performance and latency [45].…”

“…The authors of [7] propose a framework based on an improved ICP method to enhance the performance of deep learning-based classification by incorporating the shape information of the LiDAR point cloud. The authors of [24] explore a motion detection method based on point cloud registration. This method detects motion by analyzing the overlapping relationship between the registered source and target point clouds.…”

Optical Flow Based Detection and Tracking of Moving Objects for Autonomous Vehicles

“…This offers further insights to be incorporated into the downstream modules such as motion planning/prediction in the autonomous vehicle framework. The performance of the proposed technique compared to the ICP-based methods, such as the one in [21], [27], modelfree [14] and model-based indirect tracking methods [18], is evaluated in two steps. First, the compared DATMO techniques are evaluated on a synthetic dataset generated in MATLAB scenario designer, where various driving contexts are consid-ered.…”

“…But tracking all points makes the computation process expensive and limits the method in terms of the maximum number of moving objects in a scene [20]. To overcome this challenge, before tracking scanned points they are divided into static and moving categories by generating a static obstacle map (SOM) [21] or filter objects of interested with the help of deep learning methods [27].. Point cloud registration (PCR) algorithms are widely used in model-free DATMO methods. After clustering point clouds in consecutive scans, corresponding clusters are detected and PCR algorithms such as interactive closest point (ICP) [32] are applied to each set (two clusters from the same object in different time steps) to calculate precise relative motion [21]- [26].…”

High-speed spectral confocal signal collection method based on acquisition and tracking algorithm with variable window-width

“…Acquisition and tracking algorithm is a computer vision technique which is applied in a wide field, such as motion tracking [11][12] , video compression 13 , and weapon guidance 14 . The principle of acquisition and tracking algorithm is to use the features (e.g., color, shape, texture, etc.)…”