4,484 research outputs found
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
We present an efficient method for detecting anomalies in videos. Recent
applications of convolutional neural networks have shown promises of
convolutional layers for object detection and recognition, especially in
images. However, convolutional neural networks are supervised and require
labels as learning signals. We propose a spatiotemporal architecture for
anomaly detection in videos including crowded scenes. Our architecture includes
two main components, one for spatial feature representation, and one for
learning the temporal evolution of the spatial features. Experimental results
on Avenue, Subway and UCSD benchmarks confirm that the detection accuracy of
our method is comparable to state-of-the-art methods at a considerable speed of
up to 140 fps
Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update
Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the “good” models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm
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