Using HOG for Video-based Human Detection for In-building Emergency Response
Date Issued
2015
Date
2015
Author(s)
Chang, Cheng-Yi
Abstract
Human detection and tracking is an important application in intelligent transportation systems. The data of pedestrian flow could help estimate passage capacity, and collection of pedestrian trajectories could observe the movement tendencies of people passing through the area. In this thesis, we present an approach for video-based automatic people tracking and counting. This approach is expected to be utilized in in-building emergency situations. By extracting the Histograms of Oriented Gradient (HOG) features from the training dataset, the Support Vector Machine (SVM) is trained as the human detector. By combining the HOG detector with Background Subtraction, the detection process could achieve rapid and accurate detections. The Kalman Filter and the Hungarian Algorithm are utilized in this process for the establishment of human trajectory tracking. We test the system on several pedestrian datasets to validate the performance of the proposed system. The automated pedestrian counting system has shown its potential provided the tracking results.
Subjects
Human Detections
HOG
Multi-human Tracking
Human Countin
Kalman Filter
SVM classifier
Background Subtraction
SDGs
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-104-R02521528-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):93cdaa8ed3f92395ab0b7d5758439305