Anomaly detection with a moving camera using spatio-temporal codebooks

Nakahata, Mateus T.; Thomaz, Lucas A.; Silva, Allan F. da; Silva, Eduardo A. B. da; Netto, Sérgio L.

doi:10.1007/s11045-017-0486-8

Cited by 15 publications

(3 citation statements)

References 23 publications

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“…Stationary surveillance cameras have a problem of immobility that prevents the detection of location-dependent contexts. There are also studies using a pan-tilt camera [10], that lacks mobility, and moving cameras [11], [12] mounted on inspection robots. Henrio et al [13] and Singh et al [13] use a UAV that can conduct a wide range of monitoring.…”

Section: A Related Workmentioning

confidence: 99%

“…Therefore, they lack an explicit representation of anomalies. Nakahata et al [12] propose a spatio-temporal composition (STC) method to detect anomalous pixels in video frames instead of calculating an anomaly score. Unlike other methods, Kooij et al [9] use Bayesian Networks (BN) to combine audial and visual features for anomaly detection.…”

Section: A Related Workmentioning

confidence: 99%

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Context-Dependent Anomaly Detection for Low Altitude Traffic Surveillance

Bozcan¹,

Kayacan²

2021

Preprint

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The detection of contextual anomalies is a challenging task for surveillance since an observation can be considered anomalous or normal in a specific environmental context. An unmanned aerial vehicle (UAV) can utilize its aerial monitoring capability and employ multiple sensors to gather contextual information about the environment and perform contextual anomaly detection. In this work, we introduce a deep neural network-based method (CADNet) to find point anomalies (i.e., single instance anomalous data) and contextual anomalies (i.e., context-specific abnormality) in an environment using a UAV. The method is based on a variational autoencoder (VAE) with a context sub-network. The context sub-network extracts contextual information regarding the environment using GPS and time data, then feeds it to the VAE to predict anomalies conditioned on the context. To the best of our knowledge, our method is the first contextual anomaly detection method for UAV-assisted aerial surveillance. We evaluate our method on the AU-AIR dataset in a traffic surveillance scenario. Quantitative comparisons against several baselines demonstrate the superiority of our approach in the anomaly detection tasks. The codes and data will be available at https://bozcani. github.io/cadnet.

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Context-Dependent Anomaly Detection for Low Altitude Traffic Surveillance

Bozcan¹,

Kayacan²

2021

Preprint

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“…Generally speaking, a popular method is in generating a background model of the scene representation, and then the outliers of that model are treated as moving objects. In the past decades, a significant number of literatures have been published under the assumption that the camera is stationary, such as Gaussian Mixture Model used in [4], Codebook Model used in [5], and Self-Organizing Neural Network Model proposed by Maddalena and Petrosino [6]. However, the captured videos may not be static in many real applications.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Moving Object Segmentation from Stationary or Moving Camera Based on Multi-frame Homography Constraints

Cui

Jiang

Wang

2019

Sensors

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Moving object segmentation is the most fundamental task for many vision-based applications. In the past decade, it has been performed on the stationary camera, or moving camera, respectively. In this paper, we show that the moving object segmentation can be addressed in a unified framework for both type of cameras. The proposed method consists of two stages: (1) In the first stage, a novel multi-frame homography model is generated to describe the background motion. Then, the inliers and outliers of that model are classified as background trajectories and moving object trajectories by the designed cumulative acknowledgment strategy. (2) In the second stage, a super-pixel-based Markov Random Fields model is used to refine the spatial accuracy of initial segmentation and obtain final pixel level labeling, which has integrated trajectory classification information, a dynamic appearance model, and spatial temporal cues. The proposed method overcomes the limitations of existing object segmentation algorithms and resolves the difference between stationary and moving cameras. The algorithm is tested on several challenging open datasets. Experiments show that the proposed method presents significant performance improvement over state-of-the-art techniques quantitatively and qualitatively.

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