Human body part detection and multi-human tracking in surveillance videos

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Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü, Türkiye

Tezin Onay Tarihi: 2012

Öğrenci: HASAN HÜSEYİN TOPÇU

Danışman: FEHİME NİHAN ÇİÇEKLİ

Özet:

With the recent developments in Computer Vision and Pattern Recognition, surveillance applications are equipped with the capabilities of event/activity understanding and interpretation which usually require recognizing humans in real world scenes. Real world scenes such as airports, streets and train stations are complex because they involve many people, complicated occlusions and cluttered backgrounds. Although complex real world scenes exist, human detectors have the capability to locate pedestrians accurately even in complex scenes and visual trackers have the capability to track targets in cluttered environments. The integration of visual object detection and tracking, which are the fundamental features of available surveillance applications, is one of the solutions for multi-human tracking problem in crowded scenes which is studied in this thesis. In this thesis, human body part detectors, which are capable of detecting human heads and human upper body parts, are trained with Support Vector Machines (SVM) by using Histogram of Oriented Gradients (HOG), which is one of the state-of-the-art descriptor for human detection. The training process is elaborated by investigating the effects of the parameters of the HOG descriptor. The human heads and upper body parts are searched in the region of interests (ROI) computed by detecting motion. In addition, these human body part detectors are integrated with a multi-human tracker which solves the data association problem with the Multi Scan Markov Chain Monte Carlo Data Association (MCMCDA) algorithm. Associated measurements of human upper body part locations are used for state correction for each track. State estimation is done through Kalman Filter. The performance of detectors are evaluated using MIT Pedestrian dataset and INRIA Human dataset.