international conference on image analysis and recognition (iciar’09). halifax, canada, 6-8 july...
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International Conference on Image Analysis and Recognition (ICIAR’09). Halifax, Canada, 6-8 July 2009.
Video Compression and Retrieval of Moving
Object Location Applied to Surveillance
William R. Schwartz, Hélio Pedrini, Larry S. Davis
International Conference on Image Analysis and Recognition (ICIAR’09). Halifax, Canada, 6-8 July 2009. 2
Introduction Proposed Method Experimental Results Conclusions and Remarks
Organization
International Conference on Image Analysis and Recognition (ICIAR’09). Halifax, Canada, 6-8 July 2009. 3
Motivation: Surveillance cameras capture hours of data that need
to be store and analyzed.
Introduction
Analysis is based mostly on moving objects (interaction between people in the scene).
Reduce redundancy + store objects.
4Introduction Proposed method Experimental results Conclusions
Goals: Develop a video compression technique that takes
advantage of static cameras (surveillance). Provide information regarding the location of moving
objects (higher-level computer vision tasks).
Key ideas: Estimate eigenspaces for non-overlapping blocks. Use a second encoding scheme to encode regions not
well modeled by the eigenspaces.
Introduction
5Introduction Proposed method Experimental results Conclusions
Use of eigenspaces allow high compression ratio. Store the projection vectors (obtained using PCA). For a new frame, save only projection coefficients (in
case of no changes or linear changes in the block).
Advantages of Eigenspaces
Blocksize
Compression ratio (assuming float coefficients)
1 coefficient 2 coefficients 3 coefficients 4 coefficients
8x8 16 8 5.33 4
16x16 64 32 21.33 16
32x32 256 128 85.33 64
64x64 1024 512 341.33 256
6Introduction Proposed method Experimental results Conclusions
Eigenspaces do not model non-linear changes (i.e. moving person).
Use a second encoding method for these cases.
Two-Stage Scheme
7Introduction Proposed method Experimental results Conclusions
Segment the image area into non-overlapping blocks.
Estimate eigenspaces for each block. For new frames, project blocks onto the
eigenspaces and compute the reprojection error. If the reprojection error is acceptable, save the
scores, otherwise set the image block to be encoded using MPEG-4.
Proposed Method
8Introduction Proposed method Experimental results Conclusions
For each block, sample frames free of non-linear transformations (≈ 200 frames).
Estimate projection vectors P = {p1,…, pk}.
Estimate reconstruction error distribution δp for
each pixel in the block (used to locate moving objects).
Learning the Eigenspaces
10Introduction Proposed method Experimental results Conclusions
The reprojection error is high when there are non-linear transformations within a block (moving objects / non-linear local illumination changes).
Given that a block was compressed using MPEG-4, check the pixels that do not satisfy the error distribution δp to decide if there is a moving
object on that block.
Location of Moving Objects
11Introduction Proposed method Experimental results Conclusions
Our method was tested on four video sequences. Compared to MPEG-4 and H.263 using
MEncoder. Experiments:
Compression with a constant PSNR. Moving object location accuracy.
Experimental Results
12Introduction Proposed method Experimental results Conclusions
Frames were converted to YCbCr. Using blocks of 16 × 16 pixels. Initial 200 frames were used to learn the
eigenspaces. Number of PCA coefficients kept is estimated for
each block (bounded by a maximum).
Video Compression
13Introduction Proposed method Experimental results Conclusions
Video Compression
videosequence
Size &# frames
PSNR (dB)
Compression Ratio
MPEG-4 H.263 proposed
camera 1 768x288x2695 39.00 33.78 34.33 37.27
camera 2 720x528x5333 38.00 40.44 40.27 45.06
station 720x576x2370 39.50 51.27 49.94 105.64
robbery 720x480x3320 38.00 34.70 34.66 61.14
camera 2
camera 1
robbery
station
14Introduction Proposed method Experimental results Conclusions
Easy to retrieve object location due to the encoding scheme used.
Evaluation compares the location obtained by the method to the ground truth location.
At a false positive rate of 0.025 obtained a false negative rate of 0.051.
Moving Object Location
15Introduction Proposed method Experimental results Conclusions
High compression rates: Robust to linear transformations in illumination. No need for saving key-frames.
Fast to encode (≈ 5 fps in MATLAB). Useful for higher level computer vision tasks due
to the storage of moving object locations. The use of multiple size blocks might increase
the compression ratio.
Conclusions and Remarks
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