Use of probability hypothesis density filter for human activity recognition

Günay, Elif Erdem
This thesis addresses a Gaussian Mixture Probability Hypothesis Density (GMPHD) based probabilistic group tracking approach to human action recognition problem. First of all, feature set of the video images denoted as observations are obtained by applying Harris Corner Detector(HCD) technique following a GMPHD lter, which is a state-of-the-art target tracking method. Discriminative information is extracted from the output of the GM-PHD lter and using these, recognition features are constructed related to di erent body segments and the whole body. An unique Hidden Markov Model(HMM) belonging to each feature is fed by these information and recognition is performed by selecting optimal HMM's. The performance of the proposed approach is shown on the videos in KTH Research Project Database and custom videos including occlusion scenarios.The results are presented as the percentage of the correctly recognized videos. Same experiments on KTH database are performed for KLT tracker instead of GMPHD in the proposed approach. In addition, a comparison is made for an algorithm in the literature for the custom videos. The results shown that proposed approach has comparable performance on KTH database and is better in handling occlusion scenarios.


Background tracking of a video taken from a front camera of non maneuvering vehicle
Ünver, Önder; Demirekler, Mübeccel; Department of Electrical and Electronics Engineering (2014)
In this study, a novel background tracking technique is proposed that uses extended Kalman Gaussian mixture probability hypothesis density filtering approach. Since the background in a movie, taken from a front camera of a non maneuvering moving vehicle, exhibits a non-stationary nature, tracking the background is usually done by using pixel-wise comparisons in consequent frames. Besides, some methods use features of the background to track it. The proposed method uses the feature tracking approach. The fea...
Extended Target Tracking using a Gaussian-Mixture PHD Filter
Granstrom, Karl; Lundquist, Christian; Orguner, Umut (2012-10-01)
This paper presents a Gaussian-mixture (GM) implementation of the probability hypothesis density (PHD) filter for tracking extended targets. The exact filter requires processing of all possible measurement set partitions, which is generally infeasible to implement. A method is proposed for limiting the number of considered partitions and possible alternatives are discussed. The implementation is used on simulated data and in experiments with real laser data, and the advantage of the filter is illustrated. S...
A Formal Methods Approach to Pattern Recognition and Synthesis in Reaction Diffusion Networks
Bartocci, Ezio; Aydın Göl, Ebru; Haghighi, Iman; Belta, Calin (2018-03-01)
We introduce a formal framework for specifying, detecting, and generating spatial patterns in reaction diffusion networks. Our approach is based on a novel spatial superposition logic, whose semantics is defined over the quad-tree representation of a partitioned image. We demonstrate how to use rule-based classifiers to efficiently learn spatial superposition logic formulas for several types of patterns from positive and negative examples. We implement pattern detection as a model-checking algorithm and we ...
Multi-target tracking using passive doppler measurements
Guldogan, Mehmet B.; Orguner, Umut; Gustafsson, Fredrik (2013-04-26)
In this paper, we analyze the performance of the Gaussian mixture probability hypothesis density (GM-PHD) filter in tracking multiple non-cooperative targets using Doppler-only measurements in a passive sensor network. Clutter, missed detections and multi-static Doppler variances are incorporated into a realistic multi-target scenario. Simulation results show that the GM-PHD filter successfully tracks multiple targets using only Doppler shift measurements in a passive multi-static scenario.
Continuous dimensionality characterization of image structures
Felsberg, Michael; Kalkan, Sinan; Kruger, Norbert (Elsevier BV, 2009-05-04)
Intrinsic dimensionality is a concept introduced by statistics and later used in image processing to measure the dimensionality of a data set. In this paper, we introduce a continuous representation of the intrinsic dimension of an image patch in terms of its local spectrum or, equivalently, its gradient field. By making use of a cone structure and barycentric co-ordinates, we can associate three confidences to the three different ideal cases of intrinsic dimensions corresponding to homogeneous image patche...
Citation Formats
E. E. Günay, “Use of probability hypothesis density filter for human activity recognition,” Ph.D. - Doctoral Program, Middle East Technical University, 2016.