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15-463: Computational Photography- Gait-Based Human Recognition

Girish Jattani. 15-463: Computational Photography- Gait-Based Human Recognition. Motivation/Prior Work. Automatic Gait Recognition Employs statistical analysis of human walking patterns to identify humans in a scene Motion can be simplified to three segments

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15-463: Computational Photography- Gait-Based Human Recognition

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  1. Girish Jattani 15-463: Computational Photography-Gait-Based Human Recognition

  2. Motivation/Prior Work • Automatic Gait Recognition • Employs statistical analysis of human walking patterns to identify humans in a scene • Motion can be simplified to three segments • Relevant center of pixel mass for each segment can be used to determine each position relative to the other • Variance can identify particular characteristics about each segment • The legs have high variance because they are usually spread apart • The torso has low variance because it occupies almost all of it's segment • The head has little variance in the middle of it's box

  3. Overall Algorithm • Perform image subtraction for each frame in the sequence with the first frame as the reference • Work in the greyscale space • Use Otsu's thresholding algorithm to adjust for contrast differences between frames • Perform morphological filtering to fill in holes from similar colored background and foregrounds • Find all potential regions of connectivity using 8-way connectivity • Divide each region ('blob') into three equally-sized segments corresponding to the head, torso, and the legs • Perform statistical analysis

  4. Results • First row: successes • Second row: failures

  5. Results (continued)

  6. Results (continued) • Contrast plays a huge role in the detection, to the point where slight variations in camera positioning greatly affect results:

  7. Issues • Variation of contrast was too difficult to overcome even with adaptive • Structural elements used for morphological processing need to be more specific to image • Occlusion is still not fully handled using gait based analyses • Slight camera movement greatly impacts results

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