The invention discloses a mine personnel monitoring system and method based on computer vision. In the mine personnel monitoring system, a video acquisition system comprises a plurality of cameras carried on a cloud deck, and the cameras respectively acquire video data of the surrounding environment of a mine in real time and transmit the video data to a database in a cloud server in real time forstorage; The personnel identification system is used for acquiring video data stored in a cloud server database, extracting video key frames, performing personnel identification by using the deep learning neural network pre-training model, and identifying personnel; And the personnel tracking system establishes a background model for the video key frame, tracks and identifies personnel, finally synthesizes and processes the video, and transmits the video to the monitoring display through the digital-to-analog conversion device for real-time display. The mine environment personnel can be automatically identified, the manual video inspection and monitoring time is reduced, and the illegal mining behavior is efficiently judged for early warning.

mine personnel monitoring system and method based on computer vision

The invention discloses a group abnormal behavior real-time detection method. The method includes: constructing a first DCCNN structure for group abnormal behavior detection based on an STFD space-time body feature description operator and an original image frame; setting training parameters to optimize the first DCCNN structure so as to obtain a second DCCNN structure with high-level semantic features; and designing a group abnormal behavior real-time detection system according to the second DCCNN structure to determine whether a group abnormal behavior exists in the to-be-detected video stream. The system uses the STFD to extract and quantitatively describe the motion interaction information between the sub-groups, so that the recognition accuracy of the DCCNN is further improved, and meanwhile, the problem that the calculation amount of the current group abnormal behavior intelligent recognition system is large is solved. And a multi-frame optical flow map channel is replaced with a single-frame motion channel, so that the training parameters of the DCCNN are greatly reduced, and the training and recognition calculation speed of the DCCNN is increased.

Group abnormal behavior real-time detection method

The invention discloses an indoor personnel positioning and tracking algorithm based on depth convolution network, In the algorithm of indoor personnel localization and tracking, comprising inputtinga video image from a camera into a network in real time by capturing the video image, ZCA whitening performs the data preprocessing for reducing the correlation of features, based on the convolution.A deep network feature extractor is constructed by a multi-layer sparse automatic encoder to perform the average pooling, The training set network parameters and the weight parameters of the all-connected network are used to classify the test images, and the Softmax classifier is used to classify the test images, and the collected features are accurately recognized by the face recognition module,and finally the face recognition information is outputted. A method and apparatus for effectively position a target in that presence of multiple target interference or a change in the appearance of the target of the present invention. Compared with the traditional method, this algorithm can adapt to the environment faster.

Indoor personnel positioning and tracking algorithm based on depth convolution network

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Indoor personnel positioning and tracking algorithm based on depth convolution network