LSTM network-based multi-label video event detection method

A technology of video events and detection methods, applied to biological neural network models, computer components, instruments, etc., can solve problems such as increased calculation, blocked data sources, complex event sets and scenarios, etc.

Active Publication Date: 2018-03-20
TIANJIN UNIV
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  • Claims
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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

Method used

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Embodiment Construction

[0060] The following describes in detail a multi-label video event detection method based on an LSTM network of the present invention with reference to embodiments and drawings.

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

[0062] 1) Generate a model based on LSTM network from all the video image sequences in the Concurrent Event Dataset database. The database is annotated with multiple video clips of 16-42 minutes and contains the following event tags: 2305 walking, 1992 turning, eating food There are 2,527, 896 foods, 2921 mobile phones, 1,211 writing, 4756 discussions, and 278 items grabbing. These events are divided into 5435 2-second video image sequences.

[0063] Said generating a model based on LSTM network includes:

[0064] (1) Obtain the probability distribution of all tag sets corresponding to each video image sequence; including:

[0065] (1.1) For a given video image sequence x=(x 1 ,x...

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Abstract

The invention discloses an LSTM network-based multi-label video event detection method. According to the method, an LSTM network-based model is generated based on all video image sequences in a Concurrent Event dataset database. The method comprises the steps of obtaining the probability distribution of each video image sequence corresponding to all label sets and updating the network through theobtained probability distribution so as to obtain an LSTM network-based model; and obtaining the probability distribution of corresponding label sets by utilizing the LSTM network-based model for eachto-be-detected video. According to the invention, multiple event reports of a monitored video are generated through the method, so that the object monitoring and tracking process can be avoided. A brand-new network structure is designed for processing the monitored video based on a long and short-term memory network. The processing efficiency and the processing robustness of the monitored video are greatly improved. The problem that a traditional method is poor in recognition effect for multiple events occurring at the same time is solved.

Description

Technical field [0001] The 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 video is to monitor human behavior, activities, or other visual events that occur in the video. Now, there are more and more applications in the fields of military, public security, commerce and law. The development of this field is the rise 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 (such as Reference [1]) is also a factor. We all know that it is impossible for humans to continuously process large amounts of data. For this reason, errors usually occur. In addition, the resources to manually observe the output are very expensive. Therefore, how to know the content information in the video is a problem that has attracte...

Claims

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Application Information

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Patent Type & Authority Applications(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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