Organization

2. Introduction Proposed Method Experimental Results Conclusions and Remarks Organization

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.

4. 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

5. 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

6. Eigenspaces do not model non-linear changes (i.e. moving person). Use a second encoding method for these cases. Two-Stage Scheme

7. 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

8. 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

9. Compression Algorithm

10. 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

11. 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

12. 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

13. Video Compression camera 1 robbery station camera 2

14. 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

15. 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