![]() Moreover, in these highly skewed situations, it is also difficult to extract domain-specific features to identify falls. Due to the rarity of falls, it is difficult to employ supervised classification techniques to detect them. Human falls rarely occur however, detecting falls is very important from the health and safety perspective. Code and pretrained models are available at. More specifically, due to the polarized predictions, our method is able to obtain high-quality saliency maps without carefully tuning the optimal threshold, showing significant advantages in real-world applications. Comprehensive benchmarks on several popular datasets show that FLoss outperforms the state-of-the-art with a considerable margin. Consequently, the FLoss can continuously force the network to produce polarized activations. Compared to the conventional cross-entropy loss of which the gradients decrease dramatically in the saturated area, our loss function, named FLoss, holds considerable gradients even when the activation approaches the target. ![]() In this paper, we investigate an interesting issue: can we consistently use the F-measure formulation in both training and evaluation for SOD? By reformulating the standard F-measure, we propose the relaxed F-measure which is differentiable w.r.t the posterior and can be easily appended to the back of CNNs as the loss function. Then the quality of detected saliency maps is often evaluated in terms of F-measure. On both synthetic and real‐world datasets, the experimental results show that with the proposed DTA, the video anomaly detection methods achieve a better performance considering the changes in dynamic environment.Ĭurrent CNN-based solutions to salient object detection (SOD) mainly rely on the optimization of cross-entropy loss (CELoss). The proposed DTA is independent of the backbone network and can be easily incorporated into most existing video anomaly detection models to help identify the appropriate thresholds. In this paper, a dynamic thresholding algorithm (DTA) is proposed, which is fully data‐driven and capable of automatically determining thresholds such that the developed anomaly detection system can flexibly adapt to different scenarios. However, fixed threshold strategy cannot address the challenges brought by the dynamic environment, e.g. Most existing works use a fixed threshold that computes over all the testing data to determine the anomalies. By learning the normal patterns to generate frames and calculating their reconstruction error relative to the ground truth, a frame can be recognised as being abnormal if the reconstruction error exceeds a threshold. Anomaly detection is one of the most important applications in video surveillance that involves the temporal localisation of anomaly events in unannotated video sequences.
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