1. Over-head Person Counter in MATLAB ECE172A Benny Wong

2. Research Problem & Motivation  Overcrowding is quickly becoming an issue in today’s ever-expanding population. Needed a method to count people crossing a controlled area. (ie to prevent room/floor capacity over limit, or simply the owners wanted to know how many occupants are within the premises) –Can be used in controlled areas to count incoming people  Library entrances  Airport Terminals  Shopping stores

3. Related Research  People tracking using robust motion detection and estimation –Background subtract –Motion band-pass filter to reduce interlaced video noise –Semi-static Threshold (increases until subtracted image was essentially 0’s in training period before changing into a non-updating variable  Tracking and counting pedestrians in real-time using a single camera –Difference images (subtraction) –Single step of Erosion and Dilation to remove noise –Uses blobs that can change to track pedestrians when they cross each other (rectangular patch that has dynamic behavior dependent on location) somewhat keeps blobs from combining - rectangles need to overlap completely to combine.  Object detection and counting –Subtraction –User defined pattern matching size  Grid based template matching –Matches people with different images dependent on their location on a video grid

4. Approach  Chosen methods –Background subtraction  Used by every related research or something to the same effect –Image Closing operation (defined by trials)  Closes the holes within blob –Processes using datasets or position-controlled matching wouldn’t necessarily need solid blobs –Image Opening operation  Somewhat truncates limbs for the analysis step, as well as clears noise. –Band-pass filtering is not needed due to low motion noise –Region labeling  Counts objects in video frame –Length/Width based pedestrian filter (removes non-persons from count)  Slightly related to rectangular patches method; over-head view eliminates need for detection to account for depth

5. Results  Results showed robust detection of persons in ideal conditions (100%)  ideal conditions consist of: –People are walking at least half a person width/length away from each other –People are wearing bright or light colors causing a better threshold yield of their body size  Problems arise mostly when: –People are too close – forming one blob that is filtered from the person count because they fall outside of the limits that determine a region is a person –People are too small and wear dark clothes – static threshold further shrinks their size before analyzing step –People being too big would also fall outside of limits  Most problems have been minimized due to my choice of constraining limits. My constraints were obtained on a trial-basis using two people of vastly different body size (petite female, and relatively-large built male).

6. Improvements  Not use MATLAB, due to slow processing  Make it Real-Time  A better range of seeding person-sizes to further improve limits  Training period to improve threshold and determine a background for subtraction.  Use Blob-based tracking/detection, tracking is more accurate than counting every frame. (ie zero persons detected when two people are too close)

7. Lessons and Milestones  Attain final working datasets earlier –Had to work with very low-budget dataset early on  Ask more questions –Would’ve sped up the algorithm-choosing process  Get a better laptop or work on the same platform –Program ran fine on a desktop, but needed to be limited when ran on laptop.  Use a widely support AVI codec –Ran into codec problems when working on different computers that didn’t have DIVX video codec.