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Video Trajectory Classification and Anomaly Detection Using Hybrid CNN-VAE by Kumaran, Santhosh Kelathodi; Dogra, Debi Prosad; Roy, Partha Pratim; Mitra, Adway is a scholarly article available to read on EtoBox.

What is Video Trajectory Classification and Anomaly Detection Using Hybrid CNN-VAE about?

Classifying time series data using neural networks is a challenging problem when the length of the data varies. Video object trajectories, which are key to many of the visual surveillance applications, are often found to be of varying length. If such trajectories are used to understand the behavior (normal or anomalous) of moving objects, they need to be represented correctly. In this paper, we propose video object trajectory classification and anomaly detection using a hybrid Convolutional Neural Network (CNN) and Variational Autoencoder (VAE) architecture. First, we introduce a high level representation of object trajectories using color gradient form. In the next stage, a semi-supervised way to annotate moving object trajectories extracted using Temporal Unknown Incremental Clustering (TUIC), has been applied for trajectory class labeling. Anomalous trajectories are separated using t-Distributed Stochastic Neighbor Embedding (t-SNE). Finally, a hybrid CNN-VAE architecture has been used for trajectory classification and anomaly detection. The results obtained using publicly available surveillance video datasets reveal that the proposed method can successfully identify some of the

Author

Kumaran, Santhosh Kelathodi; Dogra, Debi Prosad; Roy, Partha Pratim; Mitra, Adway

Published

2018

Language

EN