CN113076772A - Abnormal behavior identification method based on full modality - Google Patents
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Abstract
The invention relates to the technical field of visual recognition, in particular to an abnormal behavior recognition method based on a full mode; collecting full-modal behavior samples and constructing an abnormal behavior sample library; the abnormal behavior characteristics are established through an algorithm, abnormal behavior personnel are automatically identified and the action track of the abnormal behavior personnel is tracked, and relevant personnel at each site are informed in real time to take corresponding measures, such as timely finding the abnormal behavior of the personnel: sudden running, falling down, pursuing and the like, and reminding the manager.
Description
Technical Field
The invention relates to the technical field of visual recognition, in particular to an abnormal behavior recognition method based on a full mode.
Background
The abnormal behaviors refer to irregular abnormal behaviors of the human body, such as sudden running, fighting, sick and falling, wandering, mass gathering of people and the like of the human body. The abnormal behavior identification technology is a technology for detecting and identifying abnormal behaviors of personnel from an acquired video sequence, can effectively prevent emergencies such as violent assaults, treading events and the like, can provide timely early warning information for monitoring personnel, and effectively assists relevant personnel to judge and take measures for abnormal conditions appearing in a monitoring scene in real time. At present, the field of human body key point detection generally tracks human body key points by using an optical flow method, so as to identify abnormal behaviors of a human body from video images of continuous frames.
The prior art can trigger early warning only when the human body is abnormal greatly, and people who are likely to break out violence such as ferocious ferocity, use special language and the like are not easy to discover. For example, in 2014, 20 minutes are allowed at 3, 1, and 21 nights, and a terrorist attack event occurs at the Kunming railway station, so that 29 people are in distress. The monitor and witness show that these terrorists dress uniformly and shout some unintelligible sentences before killing. If early warning can be carried out, more innocent lives can be saved.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an abnormal behavior identification method based on a full mode.
The technical scheme of the invention is as follows:
the abnormal behavior identification method based on the full mode is characterized by comprising the following steps: it comprises the following steps:
collecting full-modal behavior samples and constructing an abnormal behavior sample library;
secondly, detecting key points of the skeleton and recognizing the posture according to the sample data: identifying several key points of the human body, such as the head, shoulders, palm, sole;
thirdly, performing action recognition, such as running, falling, pursuing theft and fighting of the masses, according to the sample data;
fourthly, performing special language identification according to the sample data; such as foreign languages (dialects), dangerous languages, etc.;
fifthly, carrying out pedestrian attribute structurization according to the sample data: such as the color of the garment, the type of pants, the color of the backpack;
step six, establishing abnormal behavior characteristics through an algorithm, automatically identifying abnormal behavior personnel, tracking the action track of the abnormal behavior personnel, and informing relevant personnel of each site to take corresponding measures in real time, such as timely discovering the abnormal behavior of the personnel: sudden running, falling down, pursuing and the like, and reminding the manager.
Further, the collecting of the full modal behavior samples further includes expressions.
Further, the expression includes happiness, surprise, sadness, anger, disgust, and fear.
The invention has the beneficial effects that: abnormal behavior characteristics are constructed from language, expression and actual behaviors through full-modal behavior sample collection, so that dangerous behaviors are early warned in advance, managers can be assisted to strengthen people stream management, even terrorist attacks can be early warned, and innocent casualties are reduced.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
The following further describes embodiments of the present invention with reference to the accompanying drawings:
as shown in fig. 1, the abnormal behavior recognition method based on the full modality is characterized in that: it comprises the following steps:
collecting full-modal behavior samples and constructing an abnormal behavior sample library;
secondly, detecting key points of the skeleton and recognizing the posture according to the sample data: identifying several key points of the human body, such as the head, shoulders, palm, sole;
thirdly, performing action recognition, such as running, falling, pursuing theft and fighting of the masses, according to the sample data;
fourthly, performing special language identification according to the sample data; such as foreign languages (dialects), dangerous languages, etc.;
fifthly, carrying out pedestrian attribute structurization according to the sample data: such as the color of the garment, the type of pants, the color of the backpack;
step six, establishing abnormal behavior characteristics through an algorithm, automatically identifying abnormal behavior personnel, tracking the action track of the abnormal behavior personnel, and informing relevant personnel of each site to take corresponding measures in real time, such as timely discovering the abnormal behavior of the personnel: sudden running, falling down, pursuing and the like, and reminding the manager.
The collecting full modal behavior samples further comprises expressions.
The expressions include happiness, surprise, sadness, anger, disgust, and fear.
Wherein the step of collecting the full modal behavior samples is to collect the full modal behavior samples through a Dahua network monitoring camera poe high definition 200 ten thousand H265 zoom monitor probe DH-IPC-HF2230 with a 5-50mm manual zoom lens; then the data is transmitted to a Huasan (H3C) Mini S26G26 port gigabit network management switch; the switch then transmits the data to a DELL (DELL) R730/R740 server host rack-mounted two-way to a strong ERP file sharing storage.
In the first step, data are preprocessed, and a data preprocessing algorithm comprises histogram equalization, median filtering and normalization.
And step two, step three and step four, the identification function is realized by using an identification algorithm, specifically, a scene library is firstly constructed, and then the scene library is identified.
And step five, a detection algorithm is applied, specifically pedestrian detection.
In the sixth step, a re-recognition algorithm is applied, specifically, feature extraction, similarity measurement and feature tracking.
And an edge algorithm is also applied in the fifth step and the sixth step.
The foregoing embodiments and description have been presented only to illustrate the principles and preferred embodiments of the invention, and various changes and modifications may be made therein without departing from the spirit and scope of the invention as hereinafter claimed.
Claims (9)
1. The abnormal behavior identification method based on the full mode is characterized by comprising the following steps: it comprises the following steps:
collecting full-modal behavior samples and constructing an abnormal behavior sample library;
secondly, detecting skeleton key points and recognizing postures according to the sample data;
step three, performing action recognition according to the sample data;
fourthly, performing special language identification according to the sample data;
fifthly, carrying out pedestrian attribute structurization according to the sample data;
and step six, establishing abnormal behavior characteristics through an algorithm, automatically identifying abnormal behavior personnel, tracking the action track of the abnormal behavior personnel, and informing relevant personnel of each site to take corresponding countermeasures in real time.
2. The full-modality-based abnormal behavior recognition method according to claim 1, characterized in that: the collecting full modal behavior samples further comprises expressions.
3. The full-modality-based abnormal behavior recognition method according to claim 2, characterized in that: the expressions include happiness, surprise, sadness, anger, disgust, and fear.
4. The full-modality-based abnormal behavior recognition method according to claim 3, wherein: the step of collecting the full modal behavior samples is to collect the full modal behavior samples through a Dahua network monitoring camera poe high-definition 200 ten thousand H265 zoom monitor probe DH-IPC-HF2230 containing a 5-50mm manual zoom lens; then the data is transmitted to a Huasan (H3C) Mini S26G26 port gigabit network management switch; the switch then transmits the data to a DELL (DELL) R730/R740 server host rack-mounted two-way to a strong ERP file sharing storage.
5. The full-modality-based abnormal behavior recognition method according to claim 4, wherein: in the first step, data are preprocessed, and a data preprocessing algorithm comprises histogram equalization, median filtering and normalization.
6. The full-modality-based abnormal behavior recognition method according to claim 5, wherein: and step two, step three and step four, the identification function is realized by using an identification algorithm, specifically, a scene library is firstly constructed, and then the scene library is identified.
7. The full-modality-based abnormal behavior recognition method according to claim 6, wherein: and step five, a detection algorithm is applied, specifically pedestrian detection.
8. The full-modality-based abnormal behavior recognition method according to claim 7, wherein: in the sixth step, a re-recognition algorithm is applied, specifically, feature extraction, similarity measurement and feature tracking.
9. The full-modality-based abnormal behavior recognition method according to claim 8, wherein: and an edge algorithm is also applied in the fifth step and the sixth step.
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