Individual pig object detection algorithm based on Gaussian mixture model

The background models are crucially important for the object extraction for moving objects detection in a video.The Gaussian mixture model(GMM)is one of popular methods in the background models.Gaussian mixture model which applied to the pig target detection has some shortcomings such as low efficiency of algorithm,misjudgment points and ghosts.This study proposed an improved algorithm based on adaptive Gaussian mixture model,to overcome the deficiencies of the traditional Gaussian mixture model in pig object detection.Based on Gaussian mixture background model,this paper introduced two new parameters of video frames m and T0.The Gaussian distribution was scanned once every m frames,the excessive Gaussian distribution was deleted to improve the convergence speed of the model.Meanwhile,using different learning rates to suppress ghosts,a higher decreasing learning rate was adopted to accelerate the background modeling before T_(0),the background model would become stable as the time continued and a smaller learning rate could be used.In order to maintain a stable background and reduce noise interference,a fixed learning rate after T_(0) was used.Results of experiments indicated that this algorithm could quickly build the initial background model,detect the moving target pigs,and extract the complete contours of the target pigs’.The algorithm is characterized by good robustness and adaptability.

Multi target pigs tracking loss correction algorithm based on Faster R-CNN

In order to solve the problem that target tracking frames are lost during the visual tracking of pigs,this research proposed an algorithm for multi target pigs tracking loss correction based on Faster R-CNN.The video of live pigs was processed by Faster R-CNN to get the object bounding box.Then,the SURF and background difference method were combined to predict whether the target pig will be occluded in the next frame.According to the occlusion condition,the maximum value of the horizontal and vertical coordinate offset of the bounding box in the adjacent two frames of the frame image in continuous N(N is the value of the video frame rate)were calculated.When bounding boxes in a video frame are merged into one bounding box,this maximum value was used to correct the current tracking frame offset in order to achieve the purpose of solving the tracking target loss problem.The experiment results showed that the success rate range of RP Faster-RCNN in the data set was 80%-97% while in term of Faster-RCNN was 40%-85%.And the average center point error of RP Faster-RCNN was 1.46 lower than Faster-RCNN which was about 2.60.The new algorithm was characterized by good robustness and adaptability,which could solve the problem of missing tracking target and accurately track multiple targets when the targets occlude each other.