Tracking Groups of People for Video Surveillance

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Tracking Groups of People for Video Surveillance

• Xinzhen(Elaine) Wang
• Advisor Dr.Longin Latecki

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Agenda

• Introduction
• Tracking Module
• Experimental results
• Conclusion

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Introduction to tracking groups

• Goal Given a video sequence , track real groups
  of people present in the scene.
• Steps
• I Motion detection
• II Tracking module
• III Interpretation module
• ADVISOR project overview

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Motion detector

• Goal Detect mobile objects in the scene and
  classify them into moving regions.
• Detection of moving regions
• Extraction of features
• Parameters centre of gravity, position, height
  and width (calculate both in 2D and in 3D)
• Classification (labeling) of moving regions
• 8 classes of mobile objects (person, occluded
  person, group, crowd, metro train, scene object,
  noise, unknown)

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Group Tracking

• A real group
• A set of persons who are close to each other.
• A set of moving regions.
• Four particularities
• Size coherence each moving region of a group
  has the dimensions of a person or bigger if
  several persons partially overlap each other.
• Spatial coherence all moving regions inside a
  group are close to each other.

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• Characteristics (conti)
• Temporal coherence the speed of the moving
  regions inside a group cannot exceed the speed of
  a person.
• Structure coherence The number and the size of
  moving regions inside a group should be stable.

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Steps in tracking algorithm

• Tracking moving regions from frame to frame.
• Computing inside the sub-graph all possible paths
• Compute the group structure that gathers all
  these paths

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Frame to Frame Tracker

• Goal
• Link from frame to frame all moving regions
  computed by the motion detector.
• A link
• The link between Mnew and Mold is computed
  depending on their 2D and 3D distance and the
  similitude between their bounding box sizes.
• Split one Mold linked to several Mnew
• Merge several Mold linked to one Mnew.

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Frame to Frame Tracker

• O contains old moving regions, all those detected
  at tc 1 and also those did not get linked at
  the previous q frames

• N contains new moving regions detected at time tc

• F computes the links between O and N

• G computes the links between N and O

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Computing Paths

• Goal
• Select trajectories of moving regions that can
  correspond to real persons inside a group during
  a temporal window.
• Size coefficient

• If the size coefficient is bigger than the size
  of a person, then the path is likely to
  corresponding to a real person inside a group.
• To rank the paths

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Creation of Paths
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Update and Removing

• Update of Paths
• If Mlast is the last moving region added in
  and is linked to the moving region Mnew
  detected in the new frame, is duplicated
  in and extended with Mnew.
• If Mlast is not liked to any new moving region,
  the path is only duplicated. As a
  result, the rank of such a path decreases.
• Removing Paths
• Pi is totally overlapping Pj and the size of Pj
  is bigger.
• Pi does not belong to a group anymore

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Groups computing

• Goal Select the paths of a connected sub-graph
  of that best match with the trajectories of
  real persons. A group Gm is represented by its N
  paths Pm,k,

• Description
• Groups are computed with a delay T, which
  constitutes a temporal window tc T, tc of
  size T.
• In this window, first compute all possible future
  trajectories of moving regions detected at time
  tc T
• Select at time tc T the moving regions best
  match a real group that would be observed from
  time tc T to tc.

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Density of the group over time

• Group quality coefficient (q.c.)

• Instantaneous quality coefficient

• Proximity between Pm,best and Pm,k

• Distance between Pm,best and Pm,k

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Group Operations

• Creation of Group
• Selecting Nmax paths with biggest size coef,
  compute q.c.
• Check if the q.c. is higher thank a threshold.
• Update of Gm at tc - T
• Adding, extending or removing the paths composing
  the group.
• Remove all paths Pm,i too far from Pm,best
• Select the remaining paths with best size
  coefficients
• Recompute Pm,best and q.c.
• Removing Groups
• A group is removed if its quality coefficient is
  lower than a threshold.

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Experimental Results

• Tested on several metro sequences
• Longest sequence has more than 6500 frames
• Red box moving regions classified as PERSON
• Green box moving regions classified as GROUP
• Blue box moving regions tracked as a real group.
• Main limitation
• An imperfect estimation of real group size due to
  errors in motion detection.
• Over-estimation
• Under-estimation

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Conclusions

• Track correctly groups of people from beginning
  to end.
• Future development
• Computation of group trajectory, speed and events
  inside the group in order to recognize abnormal
  behaviors.

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Thank You !