36,390 research outputs found
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection
We present a novel unsupervised deep learning framework for anomalous event
detection in complex video scenes. While most existing works merely use
hand-crafted appearance and motion features, we propose Appearance and Motion
DeepNet (AMDN) which utilizes deep neural networks to automatically learn
feature representations. To exploit the complementary information of both
appearance and motion patterns, we introduce a novel double fusion framework,
combining both the benefits of traditional early fusion and late fusion
strategies. Specifically, stacked denoising autoencoders are proposed to
separately learn both appearance and motion features as well as a joint
representation (early fusion). Based on the learned representations, multiple
one-class SVM models are used to predict the anomaly scores of each input,
which are then integrated with a late fusion strategy for final anomaly
detection. We evaluate the proposed method on two publicly available video
surveillance datasets, showing competitive performance with respect to state of
the art approaches.Comment: Oral paper in BMVC 201
Online real-time crowd behavior detection in video sequences
Automatically detecting events in crowded scenes is a challenging task in Computer Vision. A number of offline approaches have been proposed for solving the problem of crowd behavior detection, however the offline assumption limits their application in real-world video surveillance systems. In this paper, we propose an online and real-time method for detecting events in crowded video sequences. The proposed approach is based on the combination of visual feature extraction and image segmentation and it works without the need of a training phase. A quantitative experimental evaluation has been carried out on multiple publicly available video sequences, containing data from various crowd scenarios and different types of events, to demonstrate the effectiveness of the approach
Future Frame Prediction for Anomaly Detection -- A New Baseline
Anomaly detection in videos refers to the identification of events that do
not conform to expected behavior. However, almost all existing methods tackle
the problem by minimizing the reconstruction errors of training data, which
cannot guarantee a larger reconstruction error for an abnormal event. In this
paper, we propose to tackle the anomaly detection problem within a video
prediction framework. To the best of our knowledge, this is the first work that
leverages the difference between a predicted future frame and its ground truth
to detect an abnormal event. To predict a future frame with higher quality for
normal events, other than the commonly used appearance (spatial) constraints on
intensity and gradient, we also introduce a motion (temporal) constraint in
video prediction by enforcing the optical flow between predicted frames and
ground truth frames to be consistent, and this is the first work that
introduces a temporal constraint into the video prediction task. Such spatial
and motion constraints facilitate the future frame prediction for normal
events, and consequently facilitate to identify those abnormal events that do
not conform the expectation. Extensive experiments on both a toy dataset and
some publicly available datasets validate the effectiveness of our method in
terms of robustness to the uncertainty in normal events and the sensitivity to
abnormal events.Comment: IEEE Conference on Computer Vision and Pattern Recognition 201
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