Graduate Exam Abstract
Hrushikesh Kulkarni
M.S. Final
June 5, 2014, 11:00 AM
CS 425
Performance evaluation of feature sets for carried object detection in still images
Abstract: Human activity recognition has gathered a lot of interest. The ability to accurately detect carried objects on human beings will directly help activity recognition. This thesis performs evaluation of four different features for carried object detection. To detect carried objects, image chips in a video are extracted by tracking moving objects using an off the shelf tracker. Pixels with similar colors are grouped together by using a superpixel segmentation algorithm. Features are calculated with respect to every superpixel, and encode information regarding their location in the track chip, shape of the superpixel, pose of the person in the track chip, and appearance of the superpixel. ROC curves are used for analyzing the detection of a superpixel as a carried object using these features individually or in a combination. These ROC curves show that the detection using Shape features as they are calculated have very less information. The location features, though simple to calculate, have a significant usable information. Detection using pose of a person in the track chip and appearance of the superpixel depend largely on the data used for their calculation. Pose detections are more likely to be correct if there are no occlusions, while appearance work better if we have high resolution of input images.
Adviser: Dr. Ross Beveridge
Co-Adviser: Dr. Bruce Draper
Non-ECE Member: Dr. David G. Alciatore
Member 3: Dr. Sudeep Pasricha
Addional Members: N/A
Publications:
N/A
Program of Study:
ECE555
ECE513
ECE666
CS510
CS410
CS612
MATH560
CS545
