Chrome Extension
WeChat Mini Program
Use on ChatGLM

A vehicle tracking algorithm combining detector and tracker

EURASIP Journal on Image and Video Processing(2020)

Cited 23|Views3
No score
Abstract
Real-time multichannel video analysis is significant for intelligent transportation. Considering that deep learning and correlation filter (CF) tracking are time-consuming, a vehicle tracking method for traffic scenes is presented based on a detection-based tracking (DBT) framework. To design the model of vehicle detection, the You Only Look Once (YOLO) model is used, and then, two constraints including object attribute information and intersection over union (IOU), are combined to modify the vehicle detection box. This approach improves vehicle detection precision. In the design of tracking model, a lightweight feature extraction network model for vehicle tracking is constructed. An inception module is used in this model to reduce the computational load and increase the adaptivity of the network scale. And a squeeze-and-excitation channel attention mechanism is adopted to enhance feature learning. Regarding the object tracking strategy, the method of combining a spatial constraint and filter template matching is adopted. The observation value and prediction value are matched and corrected to achieve stable tracking of the target. Based on the interference of occlusion in target tracking, the spatial position, moving direction, and historical feature correlation of the target are comprehensively employed to achieve continuous tracking of the target.
More
Translated text
Key words
Vehicle detection, Correlation filter tracking, Lightweight convolutional learning network
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined