04046nas|a2200421 a 4500 20260818090354.0 091112s2009 th uu|m rtt 0| a1eng d .b1207035x AIT RSPR no.CS-09-03 Ali, Irshad Detection and tracking of multiple humans in high-density crowds Pathum Thani, Thailand : Asian Institute of Technology, 2009 33 p. : ill. Research studies project report ; no. CS-09-03 Submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Computer Science, School of Engineering and Technology Research Studies Project Report (M.Eng.) - Asian Institute of Technology, 2009 As public concern about crime and terrorist activity increases, the importance of public security is growing, and video surveillance systems are increasingly widespread tools for monitoring, management, and law enforcement in public areas. The visual surveillance system has become popular research area in computer vision. There are many algorithms exists to detect and track people in video stream. Human detection and tracking in high density crowds where object occlusion is very high is still an unsolved problem. Many preprocessing techniques such as background subtraction are fail in such situations. I present a fully automatic approach to multiple human detection and tracking in high density crowds in the presence of extreme occlusion. We integrate human detection and tracking into a single framework, and introduce a confirmation-by-classification method to estimate confidence in a tracked trajectory, track humans through occlusions, and eliminate false positive detections. I search for new humans in those parts of each frame where humans have not been detected previously. I increase and decrease the weight of tracks through confirmation-by-classification process. This is helpful to remove those tracks (false positives) which not confirmed for long time. I keep the tracks which have not been confirmed only for a short period of time, to give chance to those tracks where human are occluded fully or partially for a short period of time to rejoin their tracks. We use a Viola and Jones AdaBoost cascade classifier for detection, a particle filer for tracking, and color histograms for appearance modeling. An experimental evaluation shows that our approach is capable of tracking humans in high density crowds despite occlusions. On a test set with 35.35 humans per image achieves 76.8% hit rate with 2.05 false positives per image and 8.2 missed humans per image. The results form a strong basis for farthar research. Signal detection Dailey, Matthew N. Chairperson Manukid Parnichkun Examination Committee Afzulpurkar, Nitin V. Examination Committee Higher Education Commission (HEC), Pakistan, Scholarship Donor Asian Institute of Technology Fellowship, Scholarship Donor Asian Institute of Technology. Research studies project report ; no. CS-09-03 Full-Text http://203.159.5.9/ait-thesis/detail.php?q=B11485 .b1207035x mnait u 240329 0 091112 m a u 0 mnait mnait mnarc 20 40 Barcode : 30050120615645 CREATED : 2009-12-11 RECORD # : i12623441 LPATRON : 1027713 LCHKIN : 2018-07-02 # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 5 TOT RENEW : 2 Barcode : 30050120615637 CREATED : 2009-12-11 RECORD # : i12623453 LPATRON : 1027576 LCHKIN : 2017-10-30 # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 1 TOT CHKOUT : 2 TOT RENEW : 3 Barcode : 30050120239446 CREATED : 2016-03-31 RECORD # : i1291552x LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 27878 27878 0 0 0 0 MAIN MAIN mnait 2026-08-18 50.00 0 AIT RSPR no.CS-09-03 30050120615645 2026-08-18 09:03:54 1 2026-08-18 20 0 0 0 0 MAIN MAIN mnait 2026-08-18 50.00 0 AIT RSPR no.CS-09-03 30050120615637 2026-08-18 09:03:54 2 2026-08-18 20 0 0 0 0 MAIN MAIN mnarc 2026-08-18 0 AIT RSPR no.CS-09-03 30050120239446 2026-08-18 09:03:54 1 2026-08-18 40