Anomaly Detection in Trajectories

Date

2016-05-19

Author

Ergezer, Hamza
Leblebicioğlu, Mehmet Kemal

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

150
views

0
downloads

In this work, we study the problem of anomaly detection of the trajectories of objects in a visual scene. For this purpose, we propose a novel representation for trajectories utilizing covariance features. Representing trajectories via covariance features enables us to calculate the distance between the trajectories of different lengths. After setting this proposed representation and calculation of distances, anomaly detection is achieved by sparse representations on nearest neighbours. Conducted experiments on both synthetic and real datasets show that the proposed method yields results which are outperforming or comparable with state of the art.

Subject Keywords

Covariance feature, Trajectory representation, Anomaly detection

URI

https://hdl.handle.net/11511/54253

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Anomaly Detection and Activity Perception Using Covariance Descriptor for Trajectories Ergezer, Hamza; Leblebicioğlu, Mehmet Kemal (2016-10-16) In this work, we study the problems of anomaly detection and activity perception through the trajectories of objects in crowded scenes. For this purpose, we propose a novel representation for trajectories via covariance features. Representing trajectories via feature covariance matrices enables us to calculate the distance between the trajectories of different lengths. After setting this proposed representation and calculation of distances between trajectories, anomaly detection is achieved by sparse repres...
Abnormal Crowd Behavior Detection Using Novel Optical Flow-Based Features Direkoglu, Cem; Sah, Melike; O'Connor, Noel E. (2017-09-01) In this paper, we propose a novel optical flow based features for abnormal crowd behaviour detection. The proposed feature is mainly based on the angle difference computed between the optical flow vectors in the current frame and in the previous frame at each pixel location. The angle difference information is also combined with the optical flow magnitude to produce new, effective and direction invariant event features. A one-class SVM is utilized to learn normal crowd behavior. If a test sample deviates si...
Covariance Matrix Estimation of Texture Correlated Compound-Gaussian Vectors for Adaptive Radar Detection Candan, Çağatay; Pascal, Frederic (2022-01-01) Covariance matrix estimation of compound-Gaussian vectors with texture-correlation (spatial correlation for the adaptive radar detectors) is examined. The texture parameters are treated as hidden random parameters whose statistical description is given by a Markov chain. States of the chain represent the value of texture coefficient and the transition probabilities establish the correlation in the texture sequence. An Expectation-Maximization (EM) method based covariance matrix estimation solution is given ...
Hyperspectral Anomaly Detection Method Based on Auto-encoder Bati, Emrecan; Caliskan, Akin; Koz, Alper; Alatan, Abdullah Aydın (2015-09-23) A major drawback of most of the existing hyperspectral anomaly detection methods is the lack of an efficient background representation, which can successfully adapt to the varying complexity of hyperspectral images. In this paper, we propose a novel anomaly detection method which represents the hyperspectral scenes of different complexity with the state-of-the-art representation learning method, namely auto-encoder. The proposed method first encodes the spectral image into a sparse code, then decodes the co...
Radar target detection in non-gaussian clutter Doyuran, Ülkü; Tanık, Yalçın; Department of Electrical and Electronics Engineering (2007) In this study, novel methods for high-resolution radar target detection in non-Gaussian clutter environment are proposed. In solution of the problem, two approaches are used: Non-coherent detection that operates on the envelope-detected signal for thresholding and coherent detection that performs clutter suppression, Doppler processing and thresholding at the same time. The proposed non-coherent detectors, which are designed to operate in non-Gaussian and range-heterogeneous clutter, yield higher performanc...

Citation Formats

H. Ergezer and M. K. Leblebicioğlu, “Anomaly Detection in Trajectories,” 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54253.