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Action Recognition System
Ihor Tanyenkov Igor Uspeniev
CCTV: Detect Conflict Behaviour
Detect:
1. Person pushing another person.
Pushing, punching and kicking is a
hand movement at a speed above
a configurable (not fixed) threshold
value, and ending with a touch to
another person.
2. Person fighting another person
by kicking or punching.
Requirements:
#1 Clearly visible hitting hand,
touch, participants.
#4 Strike motion projection to
camera image is distinguishable as
a fast motion.
CCTV: Falling Detection
Detect:
1. Person falling down from a
punch.
2. Person on ground getting kicked
or beat up.
3. Person on ground laying down
Requirements:
#1. Clearly visible standing person
#2. Clearly visible lying person
False Depth Perception
Fist is in frontHead is far behind
People located close in angular position to
camera, but have difference in distance
location, on RGB image looks like they
are too close or even touching. In this
case if one is moving fast(dancing,
rotating, etc, ), the other is not influenced
by these moves.
So we should analyse correlation
between movement intensity of people
that close to each other on RGB, and filter
false positives if their movements are
independent.
Occluded Participant
Frames 1, 2: Normal behaviour while good visibility
Frame 3: Hit while person is occluded
Frame 4: Fall while person is occluded
Occluded Hit
Frame 1: Normal behaviour while good visibility
Frames 2, 3, 4: Occluded hit
Power Standoff Without Fast Movements
Every single frame contains no strikes
Sequence of frames contains no fast motion
False Hit
A friendly hug, a pat on the shoulder can
be fast and even strong.
The difference from the power struggle
lies in the manner of movements, it is a
complex of movements of various parts of
the body.
False Grassing
Many (and perhaps
most) falls are not
due to blows, but
because of ridiculous
accidents
Standing Point Lower or Upper Than Ground Level
Impossible to detect
falling related to
ground level.
Problems in full body
position detection.
Fighting in the Crowd
Huge count of
persons in the field
of view
Mutual occlusion
and chaotic
movement
Performance
problems
Review of Existing Solutions
Group 1: Instant frame classification:
● Body position classification
Lots of false positives
● Motion as smoothed areas classification
Problems:
Group 2: Motion tracking in frame sequence:
● Optical flow for motion estimation and classification Frame rate
dependency
Group 3. Body matching in frame sequence:
● Body parts detection and matching
● Motion sequence classification
Used Approach Step 1: Pose Estimation and Analytical Motion
Pose estimation: Detect keypoints and connections. Challenge:
● Closely located persons with body intersections
● Dress on the body
● Hidden/occluded body parts
● Crowded scenes
Multiframe body matching
and action classification
Proposed Approach Step 2: Frame Sequence Classification
{1 0 1 01 0 1 0 1 0 0 0 . . . . . . . . }
CNN Feature extraction
{1 0 1 01 0 1 0 1 0 0 0 . . . . . . . . }
{1 0 1 01 0 1 0 1 0 0 0 . . . . . . . . }{1 0 1 01 0 1 0 1 0 0 0 . . . . . . . . }
Deep LSTM network
Extracting feature maps
Frame collection and preprocessing
Create embedding for each feature map
Build embedding sequence
Predict sequence with LSTM networks
Data Representativity and Accuracy
Datasets Variativity
Static features:
body parts,
primitives
Dynamics:
motion matching
speed estimation
Datasets Ground Truth Action classification
Challenges
Dataset quality on public artificial
data:
● Slow hits,
● Deceleration before hitting
● Fighting is only dynamics
● Poor action list scenario
● No ground truth
Dataset representativity:
● No touches
● No falling
● Little set of variativity:
○ environment
○ no crowd
○ person’s appearance
Challenges and Uncertainties
● Smoothed motion
● Occluded strike
● Spatial orientation estimation
● Performance improvement: GPU parallelism, multiple models serving,
intelligent preprocessing
● Voting system
● Dataset mining and labeling, request for proprietary datasets
Architecture design
Slow Motion: Filter of Third Level
Speed Function Reconstruction
Example of Function Differentiation
Example of Function Differentiation
Space of Equilibrium: Singularity Stability
Space of Equilibrium: Homogenous Deviation

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Embedded Fest 2019. Игорь Таненков и Игорь Успеньев. Action Recognition from Live CCTV streams

  • 1. Action Recognition System Ihor Tanyenkov Igor Uspeniev
  • 2. CCTV: Detect Conflict Behaviour Detect: 1. Person pushing another person. Pushing, punching and kicking is a hand movement at a speed above a configurable (not fixed) threshold value, and ending with a touch to another person. 2. Person fighting another person by kicking or punching. Requirements: #1 Clearly visible hitting hand, touch, participants. #4 Strike motion projection to camera image is distinguishable as a fast motion.
  • 3. CCTV: Falling Detection Detect: 1. Person falling down from a punch. 2. Person on ground getting kicked or beat up. 3. Person on ground laying down Requirements: #1. Clearly visible standing person #2. Clearly visible lying person
  • 4. False Depth Perception Fist is in frontHead is far behind People located close in angular position to camera, but have difference in distance location, on RGB image looks like they are too close or even touching. In this case if one is moving fast(dancing, rotating, etc, ), the other is not influenced by these moves. So we should analyse correlation between movement intensity of people that close to each other on RGB, and filter false positives if their movements are independent.
  • 5. Occluded Participant Frames 1, 2: Normal behaviour while good visibility Frame 3: Hit while person is occluded Frame 4: Fall while person is occluded
  • 6. Occluded Hit Frame 1: Normal behaviour while good visibility Frames 2, 3, 4: Occluded hit
  • 7. Power Standoff Without Fast Movements Every single frame contains no strikes Sequence of frames contains no fast motion
  • 8. False Hit A friendly hug, a pat on the shoulder can be fast and even strong. The difference from the power struggle lies in the manner of movements, it is a complex of movements of various parts of the body.
  • 9. False Grassing Many (and perhaps most) falls are not due to blows, but because of ridiculous accidents
  • 10. Standing Point Lower or Upper Than Ground Level Impossible to detect falling related to ground level. Problems in full body position detection.
  • 11. Fighting in the Crowd Huge count of persons in the field of view Mutual occlusion and chaotic movement Performance problems
  • 12. Review of Existing Solutions Group 1: Instant frame classification: ● Body position classification Lots of false positives ● Motion as smoothed areas classification Problems: Group 2: Motion tracking in frame sequence: ● Optical flow for motion estimation and classification Frame rate dependency Group 3. Body matching in frame sequence: ● Body parts detection and matching ● Motion sequence classification
  • 13. Used Approach Step 1: Pose Estimation and Analytical Motion Pose estimation: Detect keypoints and connections. Challenge: ● Closely located persons with body intersections ● Dress on the body ● Hidden/occluded body parts ● Crowded scenes Multiframe body matching and action classification
  • 14. Proposed Approach Step 2: Frame Sequence Classification {1 0 1 01 0 1 0 1 0 0 0 . . . . . . . . } CNN Feature extraction {1 0 1 01 0 1 0 1 0 0 0 . . . . . . . . } {1 0 1 01 0 1 0 1 0 0 0 . . . . . . . . }{1 0 1 01 0 1 0 1 0 0 0 . . . . . . . . } Deep LSTM network Extracting feature maps Frame collection and preprocessing Create embedding for each feature map Build embedding sequence Predict sequence with LSTM networks
  • 15. Data Representativity and Accuracy Datasets Variativity Static features: body parts, primitives Dynamics: motion matching speed estimation Datasets Ground Truth Action classification
  • 16. Challenges Dataset quality on public artificial data: ● Slow hits, ● Deceleration before hitting ● Fighting is only dynamics ● Poor action list scenario ● No ground truth Dataset representativity: ● No touches ● No falling ● Little set of variativity: ○ environment ○ no crowd ○ person’s appearance
  • 17. Challenges and Uncertainties ● Smoothed motion ● Occluded strike ● Spatial orientation estimation ● Performance improvement: GPU parallelism, multiple models serving, intelligent preprocessing ● Voting system ● Dataset mining and labeling, request for proprietary datasets
  • 19. Slow Motion: Filter of Third Level
  • 21. Example of Function Differentiation
  • 22. Example of Function Differentiation
  • 23. Space of Equilibrium: Singularity Stability
  • 24. Space of Equilibrium: Homogenous Deviation