Multiple Sensor Fusion and Classification for Moving Object Detection and Tracking

Abstract

International audience—The accurate detection and classification of moving objects is a critical aspect of Advanced Driver Assistance Systems (ADAS). We believe that by including the objects classification from multiple sensors detections as a key component of the object's representation and the perception process, we can improve the perceived model of the environment. First, we define a composite object representation to include class information in the core object's description. Second , we propose a complete perception fusion architecture based on the Evidential framework to solve the Detection and Tracking of Moving Objects (DATMO) problem by integrating the composite representation and uncertainty management. Finally, we integrate our fusion approach in a real-time application inside a vehicle demonstrator from the interactIVe IP European project which includes three main sensors: radar, lidar and camera. We test our fusion approach using real data from different driving scenarios and focusing on four objects of interest: pedestrian, bike, car and truck

Fusing Detected Humans in Multiple Perception Sensors Network

“…The proposed method is compared with the method used in reference [14]. Note that the fusion rules of [14] are the same as [12] and [13]. The results are provide in Fig.…”

“…To improve the navigation of robotic cars in cluster environment with obstacles, [11] propose a vision multisensor fusing framework, which fuses color, depth, and laser information consistently via both geometrical and semantic constraints. Sensor fusion is used in [12] to address the problem of object detection and tracking in occlusion scenario. The redundant data is determined if the Euclidian distance between objects is lower than threshold.…”

“…Since the PSN system consists of multiple Kinects and the FOV of two Kinects is congured to overlap partially each other, it is possible that someone staying in the overlapped region is detected by both Kinects. The fusion rules of [13] and [14] is similar with [12], they use the distances between detected objects to nd the same object but are detected by dierent Kinent cameras. This fusion rule easily lead to miss detection after fusing in cases dierent objects staying near each other.…”

Moving Obstacles Detection and Camera Pointing for Mobile Robot Applications

“…Once a good location is obtained, updating incrementally the local OG allows to detect moving objects without a prior knowledge of the targets. In [2], and as a continuity of [15], authors add vision information, in different fusion levels, to classify targets into pedestrian or cars to improve tracking. Let us mention [8] where authors present an approach to map, detect, track and classify moving object in outdoor environment using a LRF sensor.…”

Object Detection from a Vehicle Using Deep Learning Network and Future Integration with Multi-Sensor Fusion Algorithm

“…So some recent works are focusing on sensor fusion. For example, Chavez-Garcia et al [16] used LIDAR, RADAR, and camera in fusion for moving object detection in the vehicle setting. However, their image detection methodologies use HOG (histogram of oriented gradients) descriptors as feature, whose performance is much poorer than the performance of recently proposed deep learning approaches.…”