Anomaly detection in video systems has been popular over several years. It is still challenging to detect anomalies in a static object. To manage this objective, we focus on changes in the position of a stationary object in videos. In a normal scenario, the pixel values of the static object are fixed while in abnormal motion the fixed values change.We introduce a new concept to determine anomalies based on manual annotations in each video frame, only over a part of a static object in a frame such that it can be taken as a reference for the whole. Through color channel splitting we determine mask image, from which handcrafted features such as scratch area, perimeter, equivalent diameter and density are calculated. In the next step, we analyze frame-wise changes in feature values using a linear regression model, feature values are constant when the object remains stationary while there is a rise or fall in values when an object changes location. We classify feature values through anomaly scores and thresholds. In this model, we are evaluating our proposed framework on 12 real-time video datasets. Results are compared with existing techniques which are outperforming in terms of accuracy, mean square error and area under the curve.
Anomalous event recognition has a complicated definition in the complex background due to the sparse occurrence of anomalies. In this paper, we form a framework for classifying multiple anomalies present in video frames that happen in a context such as the sudden moment of people in various directions and anomalous vehicles in the pedestrian park. An attention U-net model on video frames is utilized to create a binary segmented anomalous image that classifies each anomalous object in the video. White pixels indicate the anomaly, and black pixels serve as the background image. For better segmentation, we have assigned a border to every anomalous object in a binary image. Further to distinguish each anomaly a watershed algorithm is utilized that develops multi-level gray image masks for every anomalous class. This forms a multi-class problem, where each anomalous instance is represented by a different gray color level. We use pixel values, Optical Intensity, entropy values, and Gaussian filter with sigma 5, and 7 to form a feature extraction module for training video images along with their multi-instance gray-level masks. Pixel-level localization and identification of unusual items are done using the feature vectors acquired from the feature extraction module and multi-class stack classifier model. The proposed methodology is evaluated on UCSD Ped1, Ped2 and UMN datasets that obtain pixel-level average accuracy results of 81.15%,87.26% and 82.67% respectively.
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