![]() The BG model used in the experiments is a very simple mean color value per pixel.įigure 3: Example blurred FG masks which are concatenated and vectorized into a feature vector. The FG masks are computed using a background (BG) model that is updated as the video is processed (see Figure 3). Anomaly 2 demo Patch#The foreground (FG) mask variation uses blurred FG masks for each frame in a video patch as a feature vector. The only differences between the two variations are the feature vector used to represent each video patch and the distance function used to compare two feature vectors. The distance to the nearest exemplar serves as the anomaly score. In the anomaly detection phase, the testing video is split into the same regions used during training and for each testing video patch, the nearest exemplar from its spatial region is found. We call these representative video patches, exemplars. In the model-building phase, the training (normal) videos are used to find a set of video patches (represented by feature vectors described later) for each spatial region that represent the variety of activity in that spatial region. The baseline algorithm has two phases: a training or model-building phase and a testing or anomaly detection phase. This figure shows nonoverlapping regions, but in our experiments we use overlapping regions. ![]() See Figure 2 for an illustration.įigure 2: Illustration of a grid of regions partitioning a video frame and a video patch encompassing 4 frames. In the experiments we choose H=40 pixels, W=40 pixels, T=4 or 7 frames, and s = 20 pixels. The new algorithm is very straightforward and is based on dividing the video into spatio-temporal regions which we call video patches, storing a set of exemplars to represent the variety of video patches occuring in each region, and then using the distance from a testing video patch to the nearest neighbor exemplar as the anomaly score.įirst, each video is divided into a grid of spatio-temporal regions of size H x W x T pixels with spatial step size s and temporal step size 1 frame. We describe two variations of a novel algorithm for video anomaly detection which we evaluate along with two previously published algorithms on the Street Scene dataset (described later). The blue square represents the ground truth labeled anomaly. arXiv preprint, 2017.Figure 1: Example frame from the Street Scene dataset and an example anomaly detection (red tinted pixels) found by our algorithm (a jaywalker). Unpaired image-to-image translation using cycle-consistent adversarial networks. Ganomaly: Semi-supervised anomaly detection via adversarial training. The GANomaly2D can somehow to capture the abnormal region of the bird and give the high score. As you can see, through the response of anomaly score map at the bottom region is high, some high response at bird region can be found. The inputs are the image in abnormal domain. The above image illustrates the demo result. Only the score of some patch are high since the region might hard to keep the latent feature consistent. After the iterations of training, the most area of anomaly score map is reduce to 0. The left figure is the input normal image, the middle figure is the reconstruct image by G_E and G_D, and the right figure is the anomaly score map. The above image shows the training result. Python3 demo.py -demo dataset/abnormal/ -batch_size 1 -r 2 Abnormal domain: the sunset frame with bird flying. ![]() Normal domain: the sunset frame without bird.However, a bird flies through the sky in some frames. In this dataset, the sunset scene are captured. We test this method toward the Sunset-bird-fly dataset. We also use PatchGAN to replace the original architecture of discriminator. Anomaly 2 demo generator#Moreover, the structure of encoder and decoder is revised from the generator in CycleGAN. ![]() The GANomaly2D is the 2D version of GANomaly. Anomaly 2 demo install#You should install the package from here. We use Torchvision_sunner to deal with data loading. While the anomaly item occurs in the frame, the anomaly score map will reflect the region rather than only predicting the frame is abnormal or not. In this repository, we purposed GANomaly2D to solve the anomaly item recognition problem while preserving the localization information. The computation is time-consuming if the methods are deployed into practical scenario and check the abnormality patch by patch. Even though there are some research to solve this problem toward whole patch, these methods doesn't contain the spatial information. GANomaly2D The Extended Version of GANomaly with Spatial ClueĪnomaly item detection is a critical issue in computer vision. ![]()
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