Multi object personnel detection and dynamic tracking method based on improved KCF

Factors such as insufficient illumination in coal mine roadways, drastic changes in object scale, easy obstruction of objects, and interference from mining lights lead to low success rate and accuracy in underground object detection and tracking. In order to solve the above problems, a multi object personnel detection and dynamic tracking method based on improved kernel correlation filter (KCF) algorithm is proposed. The method can avoid detection failure due to uneven lighting in complex underground environments. The SSD detection algorithm is introduced into the improved KCF algorithm to enhance the capability to detect multiple object personnel. ① The method reads the video sequence to be tracked, uses the SSD algorithm trained on the underground dataset to detect the object in the image. The method continues reading the next frame if no object is found. ② The method places the detected object into the tracker, preprocesses the image, scores all detection boxes according to the set threshold through comparison, and arranges them in descending order based on the score. The high score detection results are directly output, while the low score detection results are used to filter out bad information to improve detection speed. ③ The method clears the tracker after tracking and predicting object M frames through KCF, and then performs object detection again. By combining detection and tracking algorithms, the continuous tracking capability of the object is ensured. The experimental results show the following points. ① The final loss value of this method is stable around 1.675, and the detection results are relatively stable. ② The SSD recognition precision after training has improved by 52.7% compared to the SSD recognition precision before training. ③ The detection success rate and tracking accuracy of this method for mine personnel are 87.9% and 88.9%, respectively, which are higher than the detection success rate and tracking accuracy of the other four algorithms (KCF, CSRT, TLD, MIL). ④ This method has a high success rate when the overlap threshold is low, and until the overlap threshold is greater than 0.8, the success rate significantly decreases. This is because the environment in the mine is diverse, and it is difficult to fully match the labeled boxes. The practical application results show that this method has high applicability in complex environments such as insufficient lighting in underground coal mine roadways, drastic changes in object scale, easy obstruction, and interference from mining lights.