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Multi-target tracking method and system based on depth condition random field model

A conditional random field and multi-target tracking technology, which is applied in image data processing, instruments, computing, etc., can solve problems such as the combination of reasoning algorithms and neural networks, the difficulty of solving CRF model parameters, and the inability to use deep learning learning capabilities

Inactive Publication Date: 2019-07-23
SOUTH CENTRAL UNIVERSITY FOR NATIONALITIES
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Problems solved by technology

[0005] Aiming at the above defects or improvement needs of the prior art, the present invention provides a multi-target tracking method and system based on the depth-conditional random field model, the purpose of which is to solve the existing classic multi-target tracking method based on CRF modeling. Due to the complexity of the multi-target tracking problem, the specific distribution cannot effectively reflect the correlation of real data, which will reduce the accuracy of the target tracking results, and the difficulty of solving the parameters of the CRF model will not only cost a lot of Manpower and computing power, and the technical problems that the optimal performance of the CRF model cannot be exerted, and its reasoning algorithm is difficult to combine with the neural network, so it is impossible to form an end-to-end trainable deep neural network, so that deep learning cannot be used Strong ability to learn technical issues

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[0097] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not constitute a conflict with each other.

[0098] The present invention proposes a conditional random field (Conditional Random Fields, hereinafter referred to as CRF, referred to as CRF) framework for multi-target tracking. Compared with the existing CRF-based target tracking method, the present invention focuses on the learning of CRF potential energy and intractable CRF reasoning. A neutral network is specially designed to learn a mo...

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Abstract

The invention discloses a multi-target tracking method based on a depth condition random field model. The method comprises the steps of obtaining a multi-target tracking data set, carrying out data association on detection responses of any two continuous frames in all frames of the input video sequence; track sheet, generating a vertex according to the time relationship between any two track pieces in the obtained track piece set; calculating the matching degree between two track pieces corresponding to each vertex in the vertex set; determining a difficult vertex pair set according to a timerelationship and a position relationship between any two vertexes in the vertex set; and obtaining the appearance characteristics and position information of each difficult vertex pair in the obtaineddifficult vertex pair set, combining the appearance characteristics and motion characteristics of the difficult vertex pair into a difficult vertex pair characteristic vector, and inputting each difficult vertex pair characteristic vector into an LSTM network. According to the method, the correlation of real data in the multi-target tracking process can be effectively embodied, and the accuracy of a tracking result is high.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and more specifically, relates to a multi-target tracking method and system based on a depth conditional random field model. Background technique [0002] Multiple object tracking (MOT) has attracted much attention in the field of computer vision because of its academic potential and commercial value. The main task of MOT is to retrieve the motion trajectory of the target of interest in a given video sequence, calibrate the position of the target of interest, and identify the identity (ie ID) of each target of interest. The target of interest can be a pedestrian. , vehicles, animals, and even different components of an object. [0003] Most of the existing multi-target tracking methods are based on the data association model of detection, that is, after the target detection response of the step-by-step frames in the video is given, the target tracking is transformed into a data assoc...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/246
CPCG06T7/251G06T2207/20081G06T2207/20084G06T2207/30241
Inventor 项俊徐国寒侯建华张国帅麻建王超蓝华
Owner SOUTH CENTRAL UNIVERSITY FOR NATIONALITIES
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