Precise Position Estimation of Road Users by Extracting Object-Specific Key Points for Embedded Edge Cameras

Detecting road users and estimating accurate positions are significant in intelligent transportation systems (ITS). Most monocular camera-based systems for this purpose use 2D bounding box detectors to obtain real-time operability. However, this approach has the drawback of causing large positioning errors due to the use of upright rectangles for every type of object. To overcome this shortcoming, this paper proposes a method that improves the positioning accuracy of road users by modifying a conventional 2D bounding box detector to extract one or two additional object-specific key points. Since these key points are where the road users contact the ground plane, their accurate positions can be estimated based on the relation between the ground plane on the image and that on the map. The proposed method handles four types of road users: cars, pedestrians, cyclists (including motorcyclists), and e-scooter riders. This method is easy to implement by only adding extra heads to the conventional object detector and improves the positioning accuracy with a negligible amount of additional computational cost. In experiments, the proposed method was evaluated under various practical situations and showed a 66.5% improvement in road user position estimation. Furthermore, this method was simplified based on channel pruning and embedded on the edge camera with a Qualcomm QCS 610 System on Chip (SoC) to show its real-time capability.