EVALUATING SAMPLING-BASED TECHNIQUE FOR IMPROVED SMALL OBJECT DETECTION USING APPROPRIATE SIZES OF THE ANCHOR BOXES

Abstract Small object detection is a challenging task in computer vision due to the limited spatial information and low resolution of these objects. Existing generic object detectors demonstrate satisfactory performance on medium and large-sized objects, but they often struggle to accurately recognize small objects. The challenges arise from the low resolution and simple shape characteristics typically associated with small objects. In this paper, we propose an up sampling-based method for end-to-end tiny object identification that outperforms the existing state-of-the-art methods. We, like other contemporary approaches, first produce suggestions before labeling them. In the event of somewhat little things, we recommend tweaks to both of these procedures

VEHICLE TRACKING AND SPEED ESTIMATION UNDER MIXED TRAFFIC CONDITIONS USING YOLOV4 AND SORT: A CASE STUDY OF HANOI

This paper presents a method to estimate vehicle speed automatically, including cars and motorcycles under mixed traffic conditions from video sequences acquired with stationary cameras in Hanoi City of Vietnam. The motion of the vehicle is detected and tracked along the frames of the video sequences using YOLOv4 and SORT algorithms with a custom dataset. In the method, the distance traveled by the vehicle is the length of virtual point-detectors, and the travel time of the vehicle is calculated using the movement of the centroid over the entrance and exit of virtual point-detectors (i.e., region of interest), and then the speed is also estimated based on the traveled distance and the travel time. The results of two experimental studies showed that the proposed method had small values of MAPE (within 3%), proving that the proposed method is reliable and accurate for application in real-world mixed traffic environments like Hanoi, Vietnam