Then, we adopt a density-based measure in the histogram space to detect anomalous motion. The current implementation of our whole workflow in C++ enables us to process 1.3 frames per second with 2.5 GHz CPU, as reported in Table 7, the computation of the collection of affine motion models included. Several steps of the method are parallelizable, which, if implemented, could lead to greatly decrease the computing time. In particular, computation of per-block histograms and evaluation of LOF, which currently takes around 65% of the total execution time, can be effectively processed in parallel. The Wrong way video contains 445 frames acquired by a camera pointing toward a crowd passing by.
Each one of the scenarios of the PETS2009 dataset contains four sequences from different points of view of the scenes. We used the same number of training frames as reported in Wu et al. , that is 30 for the first scenario and 100 for the second one, to compute the LAF histograms of normal behavior. Anomalous motion in the two scenarios consists of people who suddenly start running at some time instant of the videos.
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Thus, if a data point of the feature space is assigned a low density compared to data points in its neighborhood, its outlierity measure is higher. From the motion models selected at pixels p ∈ ϒ, we obtain the affine flow . Mixtures of dynamic textures are introduced in Li et al. with conditional random fields to represent crowd behaviors. By exploiting both appearance and motion, they reported successful results on several datasets, but at the cost of sophisticated models that require intensive learning and high computation time. Interestingly, and despite these changes, the European approach seeks to make use of both the physical and legal infrastructure of the investor–state arbitration system that it aspires to replace.
With these experiments, it is clearly demonstrated that the local outlier factor is effectively of great importance in our proposed pipeline. Moreover, the use of eight translation classes shows a substantial advantage over using only four translation classes. Where Ti is the number of histograms computed over the training sequence corresponding to normal behaviors for block Bi, and ψ is defined in equation . In the proposed method described in the paper, these Ti histograms form the feature space where the LOF measure of the histogram hit is computed for a given block Bi at time t. For the baseline method, we compute the average histogram hiβ from these Ti histograms as defined in equation . We present several experiments to assess the performance of our anomalous motion detection method.
Lazarevic, A., Ertöz, L., Kumar, V., Ozgur, A., and Srivastava, J. “A comparative study of anomaly detection schemes in network intrusion detection,” in SIAM Data Mining, San Francisco. “Localized anomaly detection via hierarchical integrated activity discovery,” in AVSS, Krakow. In particular, for the UMN and UCSD dataset we take accuracy scores at the Equal Error Rate point of the ROC curve to evaluate significance. For PETS 2009, we simply use the accuracy scores provided in the “Overall” row of Table 4.
This procedure should not be confused with a truly pixelwise evaluation, but it ensures a minimal precision–recall balance. This pixel-level criterion was introduced in Mahadevan et al. , and it has been widely adopted in the crowd anomaly detection literature. The frame-level criterion simply acknowledges a correct classification if at least one true anomaly is detected in the frame. Undoubtedly, the literature related to anomalous motion detection is extensive and comprises a growing number of algorithms and tools. However, the task of accurately detecting and locating motion anomalies by being generic in the definition of what corresponds to anomaly remains an interesting challenge. Inside the current set of algorithms, we present a novel method that can be classified as feature based and data driven.
6. Computation Time
Indeed, several points of cluster C1 could be for instance incorrectly labeled as anomalous, or conversely it could be the case that hjα2 may be interpreted as an inlier. In any case, China appears less likely to support a judicialised approach to investor–state disputes given both its substantial pro-sovereignty stance in international affairs and reluctance to subject itself to supranational jurisdiction. These traditional policy stances reinforce China’s ongoing efforts to promote arbitration of investment disputes within China as part of the ’Belt and Road Initiative’. Chinese arbitral institutions are readying themselves for this role.
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In Kim and Grauman , a mixture of probabilistic principal component analysis aims at modeling normal flow patterns, estimated over patches of the training video set. The ISDS platform is a resource tool about the controversial investor-state dispute settlement mechanism in trade and investment agreements. It aims to provide the latest news, campaign tools and critical analysis to groups engaged in defeating ISDS.
- We used the ped1 subset where the normal behaviors are people walking through the campus scene at a normal speed, toward and away from the camera.
- In this paper, we focus on the problem of detecting and localizing anomalous motion in videos.
- “Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models,” in CVPR, Miami Beach.
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Reference LAF histograms are then computed in each block Bi containing moving pixels, of the first 6000 frames displaying normal behavior. We face a specific situation for formulating the anomalous motion detection criterion. We have no prior models on what should be both normal and abnormal behaviors. Training a classifier could be a possibility; it is easy to collect training examples for normal behaviors. However, we have usually few available anomalous motion examples, they can be of different kind, and in particular they may be unexpected.
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We have tested our method on several video datasets depicting different types of applications. Our anomalous motion detection-and-localization method relies on local motion classes derived from the pixelwise selected motion types. We compute block-based motion-weighted histograms of these motion https://cryptolisting.org/ classes as motion descriptors to characterize local motions. If required, overlapping blocks could be used as well to increase the spatial accuracy of the anomalous motion detection at the expense of computation load, down to a pixelwise detection with one-pixel stride in the block generation.
However, this would require to perform clustering in a high-dimensional space without knowing the number of clusters and their respective shape . Then, a more attractive approach is to compare the local density of features (i.e., histograms) around the test histogram with the local feature densities of its nearest neighbors. There are different ways to compute normal LAF histograms to populate the LAF histogram space and correctly compute the LOF criterion. If we are dealing with densely enough crowded scenes where the large majority of people undergo normal behavior and only a few local anomalous motion may appear, we can apply the online version of our method. It means that the reference LAF histograms hiβ can be computed in the very same image at every time t, since most of blocks Bi include normal behavior.
“Unsupervised abnormal crowd activity detection using semiparametric scan statistic,” in CVPRW, Portland. “Two-frame motion estimation based on polynomial expansion,” in SCIA, Halmstad. “High accuracy optical flow estimation based on a theory for warping,” in ECCV, Prague.
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- Significance of the frame-level experiments for all the presented datasets.
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The desired solution, however, has to comply with a number of requirements. First, the devised modeling has to be simple and generic enough so that it can be used in a wide range of applications. Computational performance is an important criterion looking toward real-time implementation (Lu et al., 2013), to supply on time information on where to focus when analyzing videos. Finally, an anomalous event detection at the frame level does not provide with enough information As a consequence, the method has to be able to localize motion anomalies in the video both temporally and spatially. We compare these two versions by providing ROC curves obtained on the UCSD ped1 dataset for the pixel-level evaluation criterion in Figure 12. At the same time, we provide results with a smaller number of translation classes for our full LOF-based method.
Local anomalous motion localization is inherent in the block-based proposed method and was experimentally demonstrated accurate enough. The UMN dataset includes eleven sequences of sudden escape events corresponding to three scenes . The videos depict groups of people freely walking around open spaces and performing ordinary actions inside a building lobby, which represents normal behaviors.
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For the anomalous motion localization in our pipeline , a percentile filter is applied on the classification output to accept or reject anomalous motion candidates. We take a temporal neighborhood of the block including the previous and next frames, as drawn in Figure 5. The neighborhood shape allows us to take into account that anomalous motion blocks may shift between two frames following the outlier moving object displacement. The classification label of block Bi is updated to the value of the 16th element of the binary vector formed by the initial classification labels of its space–time neighborhood. This vector comprises 19 components, that is the 18 labels of the space–time neighborhood and the initial label of the block, organized in ascending order.
This work was partially supported by Région Bretagne through a contribution to AB’s PhD student grant. For UMN, it turns out that SRC and our method are not statistically different, but our method surpasses with statistical significance SF and CI methods. Similarly, for the PETS 2009 experiments, the results of our method are significantly better than FF, CI and SF methods.