Image-Based Vehicle Tracking From Roadside Lidar Data

Abstract

<p><strong>Abstract.</strong> Vehicle tracking is of great importance in urban traffic systems, and the adoption of lidar technologies – including on-board and roadside systems – has significant potential for such applications. This research therefore proposes and develops an image-based vehicle-tracking framework from roadside lidar data to track the precise location and speed of a vehicle. Prior to tracking, vehicles are detected in point clouds through a three-step procedure. Cluster tracking then provides initial tracking results. The second tracking stage aims to provide more precise results, in which two strategies are developed and tested: frame-by-frame and model-matching strategies. For each strategy, tracking is implemented through two threads by converting the 3D point cloud clusters into 2D images relating to the plan and side views along the tracked vehicle’s trajectory. During this process, image registration is exploited in order to retrieve the transformation parameters between every image pair. Based on these transformations, vehicle speeds are determined directly based on (a) the locations of the chosen tracking point in the first strategy; (b) a vehicle model is built and tracking point locations can be calculated after matching every frame with the model in the second strategy. In contrast with other existing methods, the proposed method provides improved vehicle tracking via points instead of clusters. Moreover, tracking in a decomposed manner provides an opportunity to cross-validate the results from different views. The effectiveness of this method has been evaluated using roadside lidar data obtained by a Robosense 32-line laser scanner.</p>

Object-aware Multi-criteria Decision-Making Approach using the Heuristic data-driven Theory for Intelligent Transportation Systems

“…The excess points were processed using a triangulation-based clustering technique after completing background filtering as stated in [13]. Vehicle and non-vehicle moving points were separated and assembled using the Euclidean cluster algorithm in [14], followed by classification using the SVM algorithm. However, parameter collection remains a challenge in clustering algorithms during rush hour traffic scenes.…”

Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review

“…(2) Object point cloud segmentation and recognition: After filtering the background point cloud, it is necessary to further identify vehicles, pedestrians, and other objects from the filtered foreground point cloud. Firstly, a three-dimensional object point cloud clustering algorithm, such as the point cloud clustering method based on Euler distance [ 55 , 56 , 57 , 58 ], point density, and its variants [ 36 , 43 , 44 , 59 , 60 , 61 , 62 ], is used to accurately segment the foreground object point cloud into independent objects. Then, according to the prior knowledge of the object, several handcrafted features, such as the standard deviation and clustering dimension of the cluster point cloud, are extracted from the cluster.…”

“…Finally, some traditional classifiers, such as SVM, decision trees, and artificial neural networks, are used to realize object recognition. Zhang et al [ 55 , 56 ] used the Euclidean distance method to cluster the object point cloud after filtering the roadside background point cloud based on the farthest point; extracted 28-dimensional features, such as the vertical distribution histogram, 3D size, and 2D minimum bounding box of the cluster points; and used the SVM classifier for vehicle detection. Upon finishing clustering based on Euclidean distance, Zhang et al [ 54 ] estimated the bounding box to each cluster in which the total number of points is greater than the given threshold and used it as a candidate vehicle target for trajectory tracking.…”

Vehicle Detection under Adverse Weather from Roadside LiDAR Data

“…Point clustering means to cluster the points belonging to one object into one group. Zhang et al [ 18 ] used the Euclidean clustering extraction (ECE) algorithm for point clustering. ECE uses two parameters, the cluster size (S) and the tolerance (d), to search the points belonging to one object.…”