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
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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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