A multi-label video event detection method based on lstm network

A technology of video events and detection methods, applied in biological neural network models, computer components, instruments, etc., can solve problems such as complex event sets and scenarios, increase in calculation amount, error accumulation, etc., and improve the recognition effect of multiple events. , the effect of improving efficiency and robustness

Active Publication Date: 2021-05-18
TIANJIN UNIV
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AI Technical Summary

Problems solved by technology

For the data itself, the main challenges are low resolution, large amount of data, complex event sets and scenarios, and blocked data sources
For the method, there are mainly the following limitations: 1) Many methods rely on foreground and background segmentation technology, but this technology will cause error accumulation
2) Many methods rely on detection and tracking, however for different videos and moving objects, the robustness of detection and tracking is low
These disadvantages reduce the efficiency of time analysis
3) When the amount of data increases, the amount of calculation will increase significantly
Therefore, the two recognition methods lose the time of simultaneous occurrence

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  • A multi-label video event detection method based on lstm network
  • A multi-label video event detection method based on lstm network
  • A multi-label video event detection method based on lstm network

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Embodiment Construction

[0060] A method for detecting multi-label video events based on an LSTM network of the present invention will be described in detail below with reference to the embodiments and the accompanying drawings.

[0061] A multi-label video event detection method based on LSTM network of the present invention includes the following steps:

[0062] 1) Generate an LSTM network-based model from all video image sequences in the Concurrent Event Dataset database, which annotates multiple video clips of 16-42 minutes, and contains the following event labels: 2305 walking, 1992 turning, eating food 2,527 items, 896 items for picking up food, 2,921 items for using mobile phones, 1,211 items for writing, 4,756 items for discussion, and 278 items for grabbing items. The events were grouped into 5435 2-second video image sequences.

[0063] The described generation of an LSTM network-based model includes:

[0064] (1) Obtain the probability distribution of all label sets corresponding to each ...

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Abstract

A multi-label video event detection method based on LSTM network: generate a model based on LSTM network for all video image sequences in the Concurrent Event Dataset database, including: obtaining the probability distribution of each video image sequence corresponding to all label sets and passing the obtained The probability distribution updates the network to obtain a model based on the LSTM network; for the video to be detected, the model based on the LSTM network is used to obtain the probability distribution of the corresponding label set. The present invention generates multiple event reports of surveillance video through the method of the present invention, avoiding object monitoring and tracking process; for the processing of surveillance video, a brand-new network structure is designed based on the long-term and short-term memory network; greatly improving the efficiency and efficiency of surveillance video processing Robustness, which improves the poor recognition effect of traditional methods for multiple events occurring at the same time.

Description

technical field [0001] The present invention relates to a video event detection method. In particular, it relates to a multi-label video event detection method based on LSTM network. Background technique [0002] The purpose of surveillance footage is to monitor human behavior, activity, or other visual events that occur in the footage. Now, there are more and more applications in the fields of military, public safety, business and law. The development of this field has arisen with the increase of cheap computing power, the popularity of digital cameras, and the popularity of image sensors. In addition, the inefficiency of manual monitoring and monitoring systems (eg Ref. [1]) is also a factor. We all know that it is impossible for humans to process large amounts of data constantly. For this reason, errors usually occur. Furthermore, the resources to manually observe the output are very expensive. Therefore, how to know the content information in the video has been a p...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06N3/04
CPCG06V20/41G06V20/46G06V20/52G06N3/048
Inventor 苏育挺刘瑶瑶刘安安
Owner TIANJIN UNIV
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