13 research outputs found
Online growing neural gas for anomaly detection in changing surveillance scenes
Anomaly detection is still a challenging task for video surveillance due to complex environments and unpredictable human behaviors. Most existing approaches train offline detectors using manually labeled data and predefined parameters, and are hard to model changing scenes. This paper introduces a neural network based model called online Growing Neural Gas (online GNG) to perform an unsupervised learning. Unlike a parameter-fixed GNG, our model updates learning parameters continuously, for which we propose several online neighbor-related strategies. Specific operations, namely neuron insertion, deletion, learning rate adaptation and stopping criteria selection, get upgraded to online modes. In the anomaly detection stage, the behavior patterns far away from our model are labeled as anomalous, for which far away is measured by a time varying threshold. Experiments are implemented on three surveillance datasets, namely UMN, UCSD Ped1/Ped2 and Avenue dataset. All datasets have changing scenes due to mutable crowd density and behavior types. Anomaly detection results show that our model can adapt to the current scene rapidly and reduce false alarms while still detecting most anomalies. Quantitative comparisons with 12 recent approaches further confirm our superiority.National Natural Science Foundation of China (NSFC) [61673030, 61340046, 60875050, 60675025]; National High Technology Research and Development Program of China (863 Program) [2006AA04Z247]; Scientific Research Project of Guangdong Province [2015B010919004]; National high level talent special support programSCI(E)ARTICLE187-2016
Detecting abnormal events in video using Narrowed Normality Clusters
We formulate the abnormal event detection problem as an outlier detection
task and we propose a two-stage algorithm based on k-means clustering and
one-class Support Vector Machines (SVM) to eliminate outliers. In the feature
extraction stage, we propose to augment spatio-temporal cubes with deep
appearance features extracted from the last convolutional layer of a
pre-trained neural network. After extracting motion and appearance features
from the training video containing only normal events, we apply k-means
clustering to find clusters representing different types of normal motion and
appearance features. In the first stage, we consider that clusters with fewer
samples (with respect to a given threshold) contain mostly outliers, and we
eliminate these clusters altogether. In the second stage, we shrink the borders
of the remaining clusters by training a one-class SVM model on each cluster. To
detected abnormal events in the test video, we analyze each test sample and
consider its maximum normality score provided by the trained one-class SVM
models, based on the intuition that a test sample can belong to only one
cluster of normality. If the test sample does not fit well in any narrowed
normality cluster, then it is labeled as abnormal. We compare our method with
several state-of-the-art methods on three benchmark data sets. The empirical
results indicate that our abnormal event detection framework can achieve better
results in most cases, while processing the test video in real-time at 24
frames per second on a single CPU.Comment: Accepted at WACV 2019. arXiv admin note: text overlap with
arXiv:1705.0818