# Vulnerable Road User Detection for Roadside-Assisted Safety Protection: A Comprehensive Survey
## 3 VRU Detection Models
### Single-Sensor
#### 3.1.1 Camera-Based Detection
Additionally, this work utilized the Kalman filter to enhance the robustness against false negative detections and object occlusions. Another work also used Faster R-CNN employing transfer learning from diverse pre-trained architectures to detect pedestrians with infrastructure-based cameras [83], which proposed a solution for the occlusion challenge for onboard detection. [PERSON] et al. [84] refined the FPN with multi-scale feature fusion and attention mechanisms, which showed better performance on crowded pedestrian detection; however, the limitation of computational efficiency still existed. [PERSON] et al. [85] explored the application of CNN-based algorithms for identifying VRUs like pedestrians, motorcyclists, and cyclists from the roadside. In the experiment, the detection performance of the large-scale object was better than the small-scale object. For example, the detection performance of small objects using the YOLOv3 [21] and Faster R-CNN was worse by about 5% in mAP, compared with the detection performance of large objects. [PERSON] et al. [86] proposed a rare open-source vulnerable pedestrian dataset BGVP, and trained and tested five pre-trained models, including YOLOv4 [22], YOLOv5 [23], YOLOX [24], Faster R-CNN, and EfficientDet [87], where YOLOv4 and YOLOX performed the best. In general, these methods are still limited by low computational efficiency, small size, and heavy occlusion of the objects. Similar to 2D detection, camera-based 3D detection methods also follows the trend from CNN-based methods to Transformer-based methods. FCOS3D [31], based on CNNs, generates 3D bounding boxes from monocular images by utilizing depth estimation. In contrast, DETR3D [32] and PETR [33] adopt Transformer-based models, combining features from multiple views to improve detection precision. In recent years, the Bird's Eye View (BEV) feature space was preferred because it contains rich spatial information within the global view. BEVDepth [34] improved depth estimation by adding depth supervision, a camera-aware module, and refinement. BEVHeight [35] predicted height to the ground instead of depth, enabling distance-agnostic optimization. The Calibration-free BEV Representation (CBR) [36] eliminated the need for calibration, but at the cost of a drop in AP3D | R40. Complementary-BEV (CoBEV) [37] combined depth and height with a two-stage feature selection and BEV distillation, showing superior performance. Specifically, BEVHeight, CBR, and CoBEV were all developed based on roadside cameras. Thus, BEV-based 3D detection methods using cameras could become a new trend for VRU detection, as roadside cameras provide rich depth and height information, and many intersections are already equipped with them, reducing the need for upgrades. Regarding the variation in roadside cameras with different pitch angles and focal lengths, [PERSON] et al. [88] proposed UniMono, which addresses this 3D detection challenge by predicting an independent normalized depth that is unaffected by these variations, making the algorithm adaptable to different camera settings. In addition to the commonly detected VRUs, such as pedestrians and cyclists, some subclasses, like pedestrians using umbrellas, have also attracted attention from a small number of researchers. [PERSON] et al. [13] proposed the Parts Selective DPM to detect pedestrians carrying umbrellas, aiming to address the umbrella occlusion issue in rainy weather. [PERSON] et al. [89] employed a cascade decision tree to detect wheelchairs for a healthcare system. [PERSON] et al. [11] focused on e-scooter detection in densely populated urban areas to avoid misclassifying e-scooters as pedestrians. [PERSON] et al. [16] used YOLOv5 [23] to detect strollers. [PERSON] et al. [86] collected a VRU dataset and categorized VRUs into children, elderly individuals, and persons with disabilities. These rare subclasses of VRUs are often overlooked, yet they represent very vulnerable groups within the transportation system.