# Pedestrian Fall Detection Methods for Public Traffic Areas: A Literature Review
## 3 SOTA Methods for Fall Detection
### CV-Based Methods
[7] proposed a fall detection method based on depth image analysis. In this method, after identifying a pedestrian according to skin color pixels, the pedestrian tilt angle is obtained by searching the central line of the human silhouette and used as the main feature for fall detection. Moreover, the vertical velocity of a pedestrian is used as an assistive feature for fall detection. Owing to the consideration of the vertical velocity, this method can effectively avoid interfering actions, such as squatting and bending, which are identified as falls. [PERSON] et al. [17] proposed an approach for processing images captured by a depth camera to predict the probable inclination of an imminent fall of a pedestrian. This method first reconstructs a three-dimensional (3D) human object based on a two-dimensional image and depth information extracted by a Kinect camera. The information obtained from the depth camera is then converted to a spatial position in the geodetic coordinate system, and principal component analysis is used to calculate the 3D inclination for fall detection. The authors stated that the model's robustness was validated by simulating scenarios of partially overlapping and occluded pedestrians using multiple volunteers. However, based on the experimental scene figures provided in the paper, the obstructed pedestrians did not exhibit falling behavior. [PERSON] et al. [23] proposed an attention-guided long short-term memory (LSTM) model for fall detection in complex scenarios. In their method, YOLOv3 is used for pedestrian detection, and the detected pedestrians are tracked using the DeepSORT method. Subsequently, the output of the last convolutional layer of VGG16 is utilized, and the features of each trajectory are input to the attention-guided LSTM model for fall event prediction. They validated the proposed model using a self-constructed complex scene fall event dataset. However, the visual figures presented in the paper suggest that the challenges posed by complex scenes predominantly affect pedestrian detection and tracking, with limited consideration given to issues related to pedestrian occlusion. [PERSON] et al. [29] proposed a hybrid convolutional neural network (CNN) and LSTM-based deep learning model for abnormal behavior detection and a surveillance system that can instantly detect abnormal behavior. In their method, YOLOv3 is used to detect pedestrians, and the hybrid DeepSORT algorithm tracks pedestrians to obtain tracking trajectories from the sequence frames. Subsequently, a CNN is used to extract the action characteristics of each tracked trajectory, and an LSTM is used to build an anomalous behavior identification model to predict abnormal behavior, such as falling. When a detected abnormal behavior exceeds a certain threshold, the monitoring system triggers a warning mechanism and sends a message to the monitor. [PERSON] et al. [30] proposed a novel attention-guided fall detection algorithm. In their method, YOLOv3, block-based feature extraction, and attention modules are used to detect pedestrians. The DeepSORT algorithm is used to track each pedestrian for a trajectory containing a continuous event. A sliding window is used to store the feature maps, and a support vector machine (SVM) classifier is used to detect fall events. This method was tested on the CityPersons, Montreal Fall, and self-built datasets, achieving a pedestrian detection rate of 87.05% and an accuracy of 98.55%. Although the proposed method can keep tracking occluded pedestrians well, detection accuracy remains suboptimal for heavy occlusions. [PERSON] et al. [32] proposed a lightweight fall detection algorithm that can migrate well to an embedded platform. First, a mosaic data enhancement algorithm is used to enhance the pedestrian detection algorithm. Subsequently, GhostNet is used to replace the DSPDarknet53 backbone network of the YOLOv4 network structure.