An Animal Individual Recognition System Based on Video Tracking Technology
A technology of video tracking and recognition system, applied in image analysis, image enhancement, instrument and other directions, can solve the problems of recognition errors and trajectory association errors, and achieve the effect of strong implementability and strong practical application value.
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Embodiment 1
[0044] An individual animal identification system based on video tracking technology, which is divided into two parts, an individual animal detection part and an individual animal tracking part, which specifically includes the following steps:
[0045] (1) Animal individual testing part,
[0046] (1.1) Set the frame rate and save the video as an image;
[0047] (1.2) Faster-RCNN model training set production; (1.3) Faster-RCNN model training calls the training network, performs loss calculation, and judges whether the training is convergent;
[0048] (1.4) Repeated iterative calculations, the loss value converges, and a captive animal target detection model based on Faster-RCNN is obtained;
[0049] (2) The real-time tracking part of individual animals,
[0050] (2.1) Input the first frame image into the trained Faster-RCNN-based captive animal target detection model;
[0051] (2.2) each individual animal position of detection model output and the number of quantity initial...
Embodiment 2
[0079] The implementation environment of the present invention is TensFlow1.3.0, CUDA8.0, cuDNN5.1, OpenCV2.4.13. The specific implementation process is divided into two stages: the target detection network training stage and the specific application stage.
[0080] First, the target detection network training phase:
[0081] Step 1: Install the lens at a suitable position above the pen. The lens should be able to capture the overall image of the pen.
[0082] Step 2: Set the collection time interval and collect 30 pictures per second.
[0083] Step 3: Use Labelme software to complete the labeling of animal targets in the collected pictures, and store them in .xml format files to form a training data set for animal target detection in pens.
[0084] Step 4: Retrain the Faster-RCNN detection model using the picture training dataset of housed animals to obtain a Faster-RCNN-based captive animal target detection model.
[0085] Second, the specific application stage:
[0086...
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