Real-World Anomaly Detection in Video Using Spatio-Temporal Features Analysis for Weakly Labelled Data with Auto Label Generation

Detecting anomalies in videos is a complex task due to diverse content, noisy labeling, and a lack of frame-level labeling. To address these challenges in weakly labeled datasets, we propose a novel custom loss function in conjunction with the multi-instance learning (MIL) algorithm. Our approach utilizes the UCF Crime and ShanghaiTech datasets for anomaly detection. The UCF Crime dataset includes labeled videos depicting a range of incidents such as explosions, assaults, and burglaries, while the ShanghaiTech dataset is one of the largest anomaly datasets, with over 400 video clips featuring three different scenes and 130 abnormal events. We generated pseudo labels for videos using the MIL technique to detect frame-level anomalies from video-level annotations, and to train the network to distinguish between normal and abnormal classes. We conducted extensive experiments on the UCF Crime dataset using C3D and I3D features to test our model\u27s performance. For the ShanghaiTech dataset, we used I3D features for training and testing. Our results show that with I3D features, we achieve an 84.6% frame-level AUC score for the UCF Crime dataset and a 92.27% frame-level AUC score for the ShanghaiTech dataset, which are comparable to other methods used for similar datasets

Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning

Rich high-quality annotated data is critical for semantic segmentation learning, yet acquiring dense and pixel-wise ground-truth is both labor- and time-consuming. Coarse annotations (e.g., scribbles, coarse polygons) offer an economical alternative, with which training phase could hardly generate satisfactory performance unfortunately. In order to generate high-quality annotated data with a low time cost for accurate segmentation, in this paper, we propose a novel annotation enrichment strategy, which expands existing coarse annotations of training data to a finer scale. Extensive experiments on the Cityscapes and PASCAL VOC 2012 benchmarks have shown that the neural networks trained with the enriched annotations from our framework yield a significant improvement over that trained with the original coarse labels. It is highly competitive to the performance obtained by using human annotated dense annotations. The proposed method also outperforms among other state-of-the-art weakly-supervised segmentation methods.Comment: CIKM 2018 International Conference on Information and Knowledge Managemen