Target Identification and Tracking EnglishObject detection and trackingAbstractThe research on moving object identification and tracking algorithms is of great theoretical value and significance In th

roving public safety, and sports analysis. Object identification and tracking is a crucial part of computer vision in these fields. It involves detecting, recognizing, and tracking objects in a dynamic environment. However, object identification and tracking is a challenging task due to factors such as occlusion, illumination changes, and object appearance variation. Therefore, developing accurate and efficient algorithms for object identification and tracking is of great theoretical value and practical significance in various fields.

2. Object detection and tracking methods

2.1 Object detection

Object detection is the process of locating and recognizing objects in an image or video sequence. The HOG-SVM method is a widely used object detection method. HOG feature is a local descriptor that captures the shape and edges of an object. SVM classifier is used to classify the extracted features into different object categories. The HOG-SVM method has been applied in various object detection tasks, such as pedestrian detection, vehicle detection, and face detection.

2.2 Object tracking

Object tracking is the process of following a moving object over time in a video sequence. The Camshift algorithm is a popular object tracking method. It is based on the mean shift algorithm and can adaptively adjust the scale and orientation of the tracked object. The Camshift algorithm has been applied in various object tracking tasks, such as vehicle tracking, pedestrian tracking, and face tracking.

3. Applications of object detection and tracking

The object detection and tracking methods have been widely used in various fields. In the field of unmanned aerial vehicles (UAVs), object detection and tracking can be used for target identification, surveillance, and tracking. In the field of traffic monitoring, object detection and tracking can be used for vehicle detection, license plate recognition, and traffic flow analysis. In the field of sports analysis, object detection and tracking can be used for player tracking, ball tracking, and event detection.

4. Conclusion

In this paper, we have presented an overview of the research on object detection and tracking with computer vision. Object detection and tracking is a challenging task due to factors such as occlusion, illumination changes, and object appearance variation. The HOG-SVM and Camshift algorithms are widely used in object detection and tracking. Object detection and tracking have various applications in fields such as UAVs, traffic monitoring, and sports analysis. Further research is needed to improve the accuracy and efficiency of object detection and tracking algorithms