The invention discloses a self-adaptive scale pedestrian re-identification method based on data driving. The method comprises the first step of obtaining supporting consistency of cross domains between queried pedestrians and pedestrians to be tested through a sparse representation method, the second step of obtaining projection consistency of the cross domains between the queried pedestrians and the pedestrians to be tested through a K-nearest neighbor method, and the final step of calculating the distance between pedestrian pairs by synthesizing supporting consistency factors and projection consistency factors of the cross domains. According to the method, the scale is adjusted adaptively by reutilizing the consistency of training data at different viewing angles, so the performance of an existing algorithm can be improved, and a more accurate pedestrian re-identification result can be obtained.

Self-adaptive scale pedestrian re-identification method based on data driving

The invention discloses a double-angle sequencing optimization based pedestrian re-identification method, and belongs to the technical field of surveillance video retrieval. The double-angle sequencing optimization based pedestrian re-identification method comprises the steps of: firstly obtaining initial sequencing results through ways based on global characteristics and local characteristics; then taking an intersection set GKc(p) of front k results of the two sequencing results as initial values for inquiring a more reliable nearest neighbor; then regarding each goal gKc(j) in GKc(p) as a new inquiring factor for crossed opposite inquiry, weighting and fusing a sequence list of the opposite inquiry to obtain a whole optimized sequence list; finally, establishing a double-layer map for each gKc(j) in the GKc(p), and calculating neighbor similarity of the map to perform further rearrangement on the GKc(p) so as to further obtain a better re-identification effect.

Double-angle sequencing optimization based pedestrian re-identification method

The invention discloses a fault diagnosis method of a wind power transmission system based on a depth generation adversarial network. The fault diagnosis method of the wind power transmission system based on the depth generation adversarial network comprises the following steps: existing wind turbine generator transmission system vibration data of different kinds of working conditions under load are collected, and large wind turbine generator transmission system cross-domain sample training set is established; depth generation adversarial network modules are constructed; the depth generation adversarial network is pre-trained; and an on-line diagnosis to the large wind turbine generator transmission system is carried out by adopting trained modules. According to the fault diagnosis methodof the wind power transmission system based on the depth generation adversarial network, according to similarity and difference of data between a source domain and a target domain, the depth generation adversarial network is used to melt and rectify data between the two domains, similarity features can be extracted from cross domain data by a multi-layer stack self-coding network structure, and differences between the source domain and the target domain can be further rectified by a domain discriminator based on a softmax sorter, and a malfunction diagnosis of the target domain can be realizedby adopting rich knowledge of the source domain.

Fault diagnosis method of wind power transmission system based on depth generation adversarial network

The invention relates to the technical field of person re-identification in machine vision, and specifically relates to a person re-identification method based on block sparse representation. The person re-identification method based on block sparse representation includes the following steps: 1) dividing person training sets and test sets; 2) extracting person features including the person colorfeature and the person texture feature; 3) performing person feature transformation; 4) constructing a person re-identification model of block sparse representation; 5) making an alternating directionframework solution model; and 6) calculating the residual error to perform determination of the person identity. The person re-identification method based on block sparse representation effectively solves the traditional single camera visual angle limitation and overcomes the person re-identification difficulty under multiple camera visual angles. The person re-identification method based on block sparse representation can preferably perform modeling for the person shielding situation so as to preferably match the person. The person re-identification method based on block sparse representation has very strong robustness in attitude change, illumination influence, appearance and shielding of the person.

Pedestrian re-identification method based on block sparse representation

The invention discloses a similarity measurement based real-time target tracking algorithm. Target tracking has very high requirements on timeliness; under the current mainstream detection based tracking framework, a target position of a target in a new frame is determined based on similarity comparison between a testing sample and a target type as well as a background type. The similarity measurement based real-time target tracking algorithm comprises the steps of: firstly, collecting a target type set and a background type set in a current frame, calculating statistic characters thereof, and collecting a testing sample set in the next frame. A quantified standard for calculating the similarity between the testing sample and the types is defined in the method, and the target position is determined by finding the testing sample which is similar with the target type and dissimilar with the background type. The similarity measurement based real-time target tracking algorithm improves the tracking timeliness and tracking precision obviously.

Similarity measurement based real-time target tracking algorithm

The invention discloses a pedestrian search method for processing shield on the basis of sparse representation. According to the method, the distance measure between an inquiry pedestrian and a pedestrian to be tested is obtained through similarity matching based on blocks, then, a sparse representation method is used for obtaining the shield degree between the inquiry pedestrian and the pedestrian to be tested, and finally the distance measure and the shield degree are combined to calculate the similarity between a pedestrian pair. The method improves the matching accuracy of the same pedestrian under multiple cameras.

Pedestrian search method for processing shield on the basis of sparse representation

The invention relates to a depth feature representation method based on multiple stacked auto-encoding. Feature expressions of different hierarchical structures of a target object are acquired through constructing stacked auto-encoding networks of different structures. A shallow-layer (the number of hidden layers is smaller) neural network structure is constructed, a back-propagation method is adopted to train network parameters to enable a neural network to achieve the optimal structure, and outputs, namely the feature expressions, of a second layer of the network are acquired; more deeply hierarchical network structures are respectively established, network parameters are trained according to a similar manner, and thus outputs (the feature expressions) of corresponding layers are acquired; and fusion and selection are carried out on the above-mentioned obtained features according to a manner of feature combination and selection to acquire a hierarchical feature representation that characterizes a target. Therefore, corresponding visual tasks (image classification, identification and detection) are carried out.

Depth feature representation method based on multiple stacked auto-encoding

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Pedestrian search method for processing shield on the basis of sparse representation

Depth feature representation method based on multiple stacked auto-encoding