CN106156705A - A kind of pedestrian's anomaly detection method and system - Google Patents
A kind of pedestrian's anomaly detection method and system Download PDFInfo
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- CN106156705A CN106156705A CN201510160836.2A CN201510160836A CN106156705A CN 106156705 A CN106156705 A CN 106156705A CN 201510160836 A CN201510160836 A CN 201510160836A CN 106156705 A CN106156705 A CN 106156705A
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Abstract
The present invention relates to a kind of pedestrian's anomaly detection method, including: use path segment correlating method, the target pedestrian in frame of video is tracked；Calculate respectively the target pedestrian in frame of video and about pedestrian at the move distance of whole tracking process；Target pedestrian in frame of video according to above-mentioned calculating and about pedestrian are at the move distance of whole tracking process, it may be judged whether Deviant Behavior occurs.The invention still further relates to a kind of pedestrian's unusual checking system.The present invention is capable of detecting when that what pedestrian occurred in the process of walking hovers or stay behavior, improves the efficiency that monitoring people finder causes the reason of safety problem, has saved manpower.
Description
Technical field
The present invention relates to a kind of pedestrian's anomaly detection method and system.
Background technology
In recent years, along with safety problem is by the growing interest of society, the Deviant Behavior inspection in video
Survey more and more important.Behavior with surrounding pedestrian is inconsistent, there is the behavior hovered or stay,
And these behaviors may cause some safety problems.
By monitor video is analyzed, and then some Deviant Behavioies causing safety problem are entered
Row judges, can fall, by substantial amounts of in monitor video, the information filtering that security protection is useless, saves a large amount of
Manpower.
The detection of the current Deviant Behavior for pedestrian, it is common that pedestrian is tracked to target, obtains
Obtain the track of target pedestrian, by the behavior of the consistency detection target pedestrian of track and model of place
Abnormal, or by the model realization detection to Deviant Behavior.
Visible, current pedestrian's unusual checking mode is required for greatly setting up complicated model, so
After carry out model learning, inefficient and process complicated.
Summary of the invention
In view of this, it is necessary to a kind of pedestrian's anomaly detection method and system are provided.
The present invention provides a kind of pedestrian's anomaly detection method, and the method comprises the steps: a.
Use path segment correlating method, the target pedestrian in frame of video is tracked；Calculate the most respectively
Target pedestrian in frame of video and about pedestrian are at the move distance of whole tracking process；C. basis
Target pedestrian in the frame of video of above-mentioned calculating and about pedestrian are in the motion of whole tracking process
Distance, it may be judged whether Deviant Behavior occurs.
Wherein, described step a specifically includes: generate the track sheet of pedestrian according to described frame of video
Section；Use social relations distribution SAM feature, the path segment of the pedestrian generated be associated,
Realize the tracking to described target pedestrian.
Described surrounding pedestrian refers to: when starting to follow the tracks of to target pedestrian, is present in target pedestrian
The pedestrian within three meters, and the final destination of these pedestrian movement around and this target pedestrian's phase
With.
Described step b calculates target pedestrian in frame of video whole tracking process motion away from
Specifically include from s: at interval of N frame, utilize formula
Calculating target pedestrian's move distance, wherein, x, y are the position coordinates of target pedestrian, L
For in N frame internal object pedestrian movement's distance；Target pedestrian motion during whole tracking away from
From s it is: S=L1+L2+…+Ln。
Described step c specifically includes: to target pedestrian and about pedestrian in whole tracking process
Move distance calculated by beta function；By the move distance s of this target pedestrian and calculating
To the value of beta function carry out mathematic interpolation；If difference is more than threshold value T set in advance, then sentence
Break as there being Deviant Behavior to occur；If as difference less than threshold value T set in advance, then it is judged as nothing
Deviant Behavior occurs.
The present invention also provides for a kind of pedestrian's unusual checking system, this system include tracking module,
Computing module and judge module, wherein: described tracking module is used for using path segment correlating method,
Target pedestrian in frame of video is tracked；Described computing module is for calculating in frame of video respectively
Target pedestrian and about pedestrian at the move distance of whole tracking process；Described judge module is used
Target pedestrian in the frame of video according to above-mentioned calculating and about pedestrian are in whole tracking process
Move distance, it may be judged whether occur Deviant Behavior.
Wherein, described tracking module specifically for: according to described frame of video generate pedestrian track sheet
Section；Use social relations distribution SAM feature, the path segment of the pedestrian generated be associated,
Realize the tracking to described target pedestrian.
Described surrounding pedestrian refers to: when starting to follow the tracks of to target pedestrian, is present in target pedestrian
The pedestrian within three meters, and the final destination of these pedestrian movement around and this target pedestrian's phase
With.
Described computing module calculates the motion in whole tracking process of the target pedestrian in frame of video
Distance s is particularly as follows: at interval of N frame, utilize formula
Calculating target pedestrian's move distance, wherein, x, y are the position coordinates of target pedestrian, L
For in N frame internal object pedestrian movement's distance；Target pedestrian motion during whole tracking away from
From s it is: S=L1+L2+…+Ln。
Described judge module specifically for: to target pedestrian and about pedestrian in whole tracking process
Move distance calculated by beta function；By the move distance s of this target pedestrian and calculating
To the value of beta function carry out mathematic interpolation；If difference is more than threshold value T set in advance, then sentence
Break as there being Deviant Behavior to occur；If as difference less than threshold value T set in advance, then it is judged as nothing
Deviant Behavior occurs.
One pedestrian's anomaly detection method of the present invention and system, based on to target pedestrian with
Track, not by the model realization detection to Deviant Behavior, but by compare pedestrian with about
The inconsistent unusual checking that carries out of pedestrian movement, thus avoid the mistake of complex model study
Journey.The present invention is capable of detecting when that what pedestrian occurred in the process of walking hovers or stay behavior, improves
Monitoring people finder causes the efficiency of the reason of safety problem, has saved manpower.
Accompanying drawing explanation
Fig. 1 is the flow chart of the present invention a kind of pedestrian anomaly detection method；
Fig. 2 is the hardware structure figure of the present invention a kind of pedestrian unusual checking system.
Detailed description of the invention
Below in conjunction with the accompanying drawings and specific embodiment the present invention is further detailed explanation.
Refering to shown in Fig. 1, it it is the present invention a kind of pedestrian anomaly detection method preferred embodiment
Operation process chart.
Step S1, uses path segment correlating method, is tracked the target pedestrian in frame of video.
Specifically:
The first step, according to the path segment of described frame of video generation pedestrian:
By HOG (Histogram of Oriented Gradient, histograms of oriented gradients) method
In described frame of video, carry out pedestrian detection, then by optical flow method, pedestrian is tracked.Due to
The existence blocked, optical flow method is followed the tracks of and is easy to stop, generating the path segment of pedestrian.
Second step, uses social relations distribution SAM (social affinity map) feature, to life
The path segment of the pedestrian become is associated, it is achieved the tracking to described target pedestrian:
Wherein, described social relations (social affinity) refers to: the motion of pedestrian around is closed
System, social relations can be made up of friend, relative, work buddies, such as couple walking,
Leader-follower phenomenon.
First, the path segment of the pedestrian generated is carried out vector quantization, obtains this path segment
SAM feature.Wherein, the path segment of the pedestrian of described generation comprises pedestrian to be followed the tracks of, i.e.
The path segment of target pedestrian.Then, to around the path segment of synchronization this target pedestrian
A range of path segment is clustered according to SAM feature by clustering method.Wherein,
Range size is usually arranged as 3 meters, so can avoid some exceptional values.It should be noted that
The direction of motion of a range of path segment and time around the path segment of this target pedestrian
Should be consistent with this path segment.
Then, the result of above-mentioned cluster is described with a radial histogram, according to SAM feature
This radial histogram is divided into ten regions, namely ten classifications, this footpath simultaneously by modal classification
The locus distribution of described ten classifications is shown to histogram table.
Then, described radial histogram is carried out binary vector, obtain the arrow of SAM feature
Amount.
Finally, by Markov chain model (Markov-chain model) to above-mentioned path segment
It is associated, when above-mentioned path segment associates, by Hamming distance (Hamming distance)
The relatively vector of the SAM feature of two path segment, in order to by motion at similar social relations
Two path segment in distribution are associated, and form the long track of target, finally realize target
The tracking of pedestrian.
Step S2, calculate respectively the target pedestrian in frame of video and about pedestrian followed the tracks of whole
The move distance of journey.
It should be noted that the present embodiment based on social relations, so in track fragment association
Time, relate to around target pedestrian the track of pedestrian within three meters.
Around target pedestrian described in the present embodiment, pedestrian refers to: target pedestrian is starting tracking
Time, it is present in the pedestrian within three meters around target pedestrian, and the final mesh of these pedestrian movement
Ground identical with this target pedestrian.
Illustrate as a example by the concrete calculating of target pedestrian's move distance below:
Target pedestrian's move distance of described calculating refers to target pedestrian fortune during whole tracking
Dynamic distance.
At interval of N frame, calculating target pedestrian's move distance, formula is as follows:
Distance s of target pedestrian motion during whole tracking is:
S=L1+L2+…+Ln。
The computational methods of pedestrian movement's distance and above-mentioned target pedestrian movement around described target pedestrian
The computational methods of distance are similar to, and repeat no more here.
Step S3, according to the target pedestrian in the frame of video of above-mentioned calculating and about pedestrian whole
The move distance of tracking process, it may be judged whether Deviant Behavior occurs.Specifically:
To target pedestrian and about pedestrian entered by beta function at the move distance of whole tracking process
Row calculates, and beta function can also be that variance calculates function or mean value calculation function.Again should
The move distance s of target pedestrian and the value of calculated beta function carry out mathematic interpolation, if poor
Value more than threshold value T set in advance, is then judged as having Deviant Behavior to occur, if difference is less than in advance
Threshold value T set, then be judged as that behavior without exception occurs.
Refering to shown in Fig. 2, it it is the hardware structure figure of the present invention a kind of pedestrian unusual checking system.
This system includes tracking module, computing module and judge module.
Described tracking module is used for using path segment correlating method, to the target pedestrian in frame of video
It is tracked.Specifically:
The first step, according to the path segment of described frame of video generation pedestrian:
By HOG (Histogram of Oriented Gradient, histograms of oriented gradients) method
In described frame of video, carry out pedestrian detection, then by optical flow method, pedestrian is tracked.Due to
The existence blocked, optical flow method is followed the tracks of and is easy to stop, generating the path segment of pedestrian.
Second step, uses social relations distribution SAM (social affinity map) feature, to life
The path segment of the pedestrian become is associated, it is achieved the tracking to described target pedestrian:
Wherein, described social relations (social affinity) refers to: the motion of pedestrian around is closed
System, social relations can be made up of friend, relative, work buddies, such as couple walking,
Leader-follower phenomenon.
First, the path segment of the pedestrian generated is carried out vector quantization, obtains this path segment
SAM feature.Wherein, the path segment of the pedestrian of described generation comprises pedestrian to be followed the tracks of, i.e.
The path segment of target pedestrian.Then, to around the path segment of synchronization this target pedestrian
A range of path segment is clustered according to SAM feature by clustering method.Wherein,
Range size is usually arranged as 3 meters, so can avoid some exceptional values.It should be noted that
The direction of motion of a range of path segment and time around the path segment of this target pedestrian
Should be consistent with this path segment.
Then, the result of above-mentioned cluster is described with a radial histogram, according to SAM feature
This radial histogram is divided into ten regions, namely ten classifications, this footpath simultaneously by modal classification
The locus distribution of described ten classifications is shown to histogram table.
Then, described radial histogram is carried out binary vector, obtain the arrow of SAM feature
Amount.
Finally, by Markov chain model (Markov-chain model) to above-mentioned path segment
It is associated, when above-mentioned path segment associates, by Hamming distance (Hamming distance)
The relatively vector of the SAM feature of two path segment, in order to by motion at similar social relations
Two path segment in distribution are associated, and form the long track of target, finally realize target
The tracking of pedestrian.
Described computing module for calculate respectively the target pedestrian in frame of video and about pedestrian exist
The move distance of whole tracking process.
It should be noted that the present embodiment based on social relations, so in track fragment association
Time, relate to around target pedestrian the track of pedestrian within three meters.
Around target pedestrian described in the present embodiment, pedestrian refers to: target pedestrian is starting tracking
Time, it is present in the pedestrian within three meters around target pedestrian, and the final mesh of these pedestrian movement
Ground identical with this target pedestrian.
Illustrate as a example by the concrete calculating of target pedestrian's move distance below:
Target pedestrian's move distance of described calculating refers to target pedestrian fortune during whole tracking
Dynamic distance.
At interval of N frame, calculating target pedestrian's move distance, formula is as follows:
Distance s of target pedestrian motion during whole tracking is:
S=L1+L2+…+Ln。
The computational methods of pedestrian movement's distance and above-mentioned target pedestrian movement around described target pedestrian
The computational methods of distance are similar to, and repeat no more here.
Described judge module is for according to the target pedestrian in the frame of video of above-mentioned calculating and about
Pedestrian is at the move distance of whole tracking process, it may be judged whether Deviant Behavior occurs.Specifically:
To target pedestrian and about pedestrian entered by beta function at the move distance of whole tracking process
Row calculates, and beta function can also be that variance calculates function or mean value calculation function.Again should
The move distance s of target pedestrian and the value of calculated beta function carry out mathematic interpolation, if poor
Value more than threshold value T set in advance, is then judged as having Deviant Behavior to occur, if difference is less than in advance
Threshold value T set, then be judged as that behavior without exception occurs.
Although the present invention is described with reference to current better embodiment, but the technology of this area
Personnel will be understood that above-mentioned better embodiment, only for the present invention is described, not is used for limiting this
The protection domain of invention, any within the scope of the spirit and principles in the present invention, that is done any repaiies
Decorations, equivalence replacement, improvement etc., within should be included in the scope of the present invention.
Claims (10)
1. pedestrian's anomaly detection method, it is characterised in that the method comprises the steps:
A. use path segment correlating method, the target pedestrian in frame of video is tracked；
Calculate the most respectively the target pedestrian in frame of video and about pedestrian in the fortune of whole tracking process
Dynamic distance；
C. according to the target pedestrian in the frame of video of above-mentioned calculating and about pedestrian followed the tracks of whole
The move distance of journey, it may be judged whether Deviant Behavior occurs.
2. the method for claim 1, it is characterised in that described step a specifically includes:
The path segment of pedestrian is generated according to described frame of video；
Use social relations distribution SAM feature, the path segment of the pedestrian generated be associated,
Realize the tracking to described target pedestrian.
3. the method for claim 1, it is characterised in that described surrounding pedestrian refers to:
When starting to follow the tracks of to target pedestrian, it is present in the pedestrian within three meters around target pedestrian, and
The final destination of these pedestrian movement is identical with this target pedestrian.
4. method as claimed in claim 3, it is characterised in that calculate in described step b and regard
Frequently the target pedestrian in frame specifically includes at the move distance s of whole tracking process:
At interval of N frame, utilize formula Calculate once
Target pedestrian's move distance, wherein, x, y are the position coordinates of target pedestrian, and L is at N frame
Internal object pedestrian movement's distance；
Distance s of target pedestrian motion during whole tracking is: S=L1+L2+…+Ln。
5. method as claimed in claim 4, it is characterised in that described step c specifically includes:
To target pedestrian and about pedestrian entered by beta function at the move distance of whole tracking process
Row calculates；
The move distance s of this target pedestrian and the value of calculated beta function are carried out mathematic interpolation；
If difference is more than threshold value T set in advance, then it is judged as having Deviant Behavior to occur；If
Difference is less than threshold value T set in advance, then be judged as that behavior without exception occurs.
6. pedestrian's unusual checking system, it is characterised in that this system include tracking module,
Computing module and judge module, wherein:
Described tracking module is used for using path segment correlating method, to the target pedestrian in frame of video
It is tracked；
Described computing module for calculate respectively the target pedestrian in frame of video and about pedestrian exist
The move distance of whole tracking process；
Described judge module is for according to the target pedestrian in the frame of video of above-mentioned calculating and about
Pedestrian is at the move distance of whole tracking process, it may be judged whether Deviant Behavior occurs.
7. system as claimed in claim 6, it is characterised in that described tracking module specifically for:
The path segment of pedestrian is generated according to described frame of video；
Use social relations distribution SAM feature, the path segment of the pedestrian generated be associated,
Realize the tracking to described target pedestrian.
8. system as claimed in claim 6, it is characterised in that described surrounding pedestrian refers to:
When starting to follow the tracks of to target pedestrian, it is present in the pedestrian within three meters around target pedestrian, and
The final destination of these pedestrian movement is identical with this target pedestrian.
9. system as claimed in claim 8, it is characterised in that calculate in described computing module and regard
Frequently the target pedestrian in frame whole tracking process move distance s particularly as follows:
At interval of N frame, utilize formula Calculate once
Target pedestrian's move distance, wherein, x, y are the position coordinates of target pedestrian, and L is at N frame
Internal object pedestrian movement's distance；
Distance s of target pedestrian motion during whole tracking is: S=L1+L2+…+Ln。
10. system as claimed in claim 9, it is characterised in that described judge module specifically for:
To target pedestrian and about pedestrian entered by beta function at the move distance of whole tracking process
Row calculates；
The move distance s of this target pedestrian and the value of calculated beta function are carried out mathematic interpolation；
If difference is more than threshold value T set in advance, then it is judged as having Deviant Behavior to occur；If
If difference is less than threshold value T set in advance, then it is judged as that behavior without exception occurs.
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