Award Date

December 2015

Degree Type

Thesis

Degree Name

Master of Science in Electrical Engineering (MSEE)

Department

Electrical Engineering

First Committee Member

Brendan Morris

Second Committee Member

Shahram Latifi

Third Committee Member

Ebrahim Saberinia

Fourth Committee Member

Alexander Paz

Number of Pages

71

Abstract

The main objective behind this thesis is to examine how existing vision-based detection and tracking algorithms perform in thermal imagery-based video surveillance. While color-based surveillance has been extensively studied, these techniques can not be used during low illumination, at night, or with lighting changes and shadows which limits their applicability. The main contributions in this thesis are (1) the creation of a new color-thermal dataset, (2) a detailed performance comparison of different color-based detection and tracking algorithms on thermal data and (3) the proposal of an adaptive neural network for false detection rejection.

Since there are not many publicly available datasets for thermal-video surveillance, a new UNLV Thermal Color Pedestrian Dataset was collected to evaluate the performance of popular color-based detection and tracking in thermal images. The dataset provides an overhead view of humans walking through a courtyard and is appropriate for aerial surveillance scenarios such as unmanned aerial systems (UAS). Three popular detection schemes are studied for thermal pedestrian detection: 1) Haar-like features, 2) local binary pattern (LBP) and 3) background subtraction motion detection. A i) Kalman filter predictor and ii) optical flow are used for tracking. Results show that combining Haar and LBP detections with a 50% overlap rule and tracking using Kalman filters can improve the true positive rate (TPR) of detection by 20%. However, motion-based methods are better at rejecting false positive in non-moving camera scenarios. The Kalman filter with LBP detection is the most efficient tracker but optical flow better rejects false noise detections. This thesis also presents a technique for learning and characterizing pedestrian detections with "heat maps" and an object-centric motion compensation method for UAS. Finally, an adaptive method to reject false detections using error back propagation using a neural network. The adaptive rejection scheme is able to successfully learn to identify static false detections for improved detection performance.

Keywords

apperance based methods; Kalman filter; motion based methods; multi-target tracking; optical flow; Thermal image

Disciplines

Computer Sciences | Electrical and Computer Engineering

Language

English


Share

COinS