Detecting anomalies in video surveillance, particularly for firearm detection, remains a critical challenge in public safety systems. Traditional methods often rely on human operators manually monitoring surveillance feeds, which is both inefficient and prone to error. Recent advances in deep learning (DL) offer a promising alternative by enabling models to identify anomalous events without relying on rare and difficult-to-obtain positive samples during training. In this paper, we propose an unsupervised firearm detection framework based on adversarial autoencoders (AAE) networks. By learning a robust representation of normal (negative) training data, the model is able to identify deviations indicative of firearm-related anomalies during inference. Anomaly scores for firearm detection are calculated using reconstruction errors, based on the probability that the test sample aligns with the prior distribution. Our approach enhances the interpretability of firearm related anomaly detection (AD) and demonstrates superior performance on benchmark firearm datasets. Experimental results demonstrate that the proposed method surpasses current state-of-the-art techniques by effectively identifying out-of-distribution (OOD) events in video frames, leveraging learned priors within the AAE architecture. Experimental results on benchmark firearm datasets, including VISILAB, UCF-Firearm, and YouTube demonstrate the effectiveness of our firearm detection approach, achieving an average precision (AP) of 95.2% and an average detection accuracy (ACC) of 95.6%.

Video surveillance systems can play a critical role in ensuring public safety by assisting in the early detection of potentially dangerous objects or actions, such as people carrying handguns or other weapons. Recent machine learning architectures based on multi-head self-attention modules have demonstrated their ability to process sequential data. In this work, we propose AWARE, a self-Attention based Weapon Activity Recognition architecture for video surveillance systems. The main idea behind this approach is to use a Transformer encoder module to extract relevant features from input video sequences and classify them as either weapon-related actions or non-dangerous actions. The input data is generated by combining 2D human pose keypoints and potential weapon locations generated by object detection models. We evaluate our proposed method on a new action recognition dataset composed of video sequences of gun-related actions. Experiments conducted show that the proposed method achieves better results than other similar methods in this context.

This paper investigates data-free class-incremental learning (DFCIL) for hand gesture recognition from 3D skeleton sequences. In this class-incremental learning (CIL) setting, while incrementally registering the new classes, we do not have access to the training samples (i.e. data-free) of the already known classes due to privacy. Existing DFCIL methods primarily focus on various forms of knowledge distillation for model inversion to mitigate catastrophic forgetting. Unlike SOTA methods, we delve deeper into the choice of the best samples for inversion. Inspired by the well-grounded theory of max-margin classification, we find that the best samples tend to lie close to the approximate decision boundary within a reasonable margin. To this end, we propose BOAT-MI – a simple and effective boundary-aware prototypical sampling mechanism for model inversion for DFCIL. Our sampling scheme outperforms SOTA methods significantly on two 3D skeleton gesture datasets, the publicly available SHREC 2017, and EgoGesture3D – which we extract from a publicly available RGBD dataset. Both our codebase and the EgoGesture3D skeleton dataset are publicly available: https://github.com/humansensinglab/dfcil-hgr.

Early detection of the presence of dangerous objects such as handguns in Closed-Circuit Television (CCTV) images is vital to reduce the potential damage. In this work, a novel method for automatic detection of handguns in CCTV-like images based on a combination architecture which leverages body pose estimation is proposed. Weapon appearance features along with body pose features are combined to perform robust detection in typical surveillance environments where appearance features alone are not sufficient (e.g., because the handgun may appear too small or dark). Both CNN and recent transformer-based architectures are applied for visual feature extraction. Experiments on multiple datasets show that this approach improves state-of-the-art pose-based handgun detectors. An ablation study is also performed to verify the contribution of the pose processing branch and the false positive filter.

Soil-transmitted helminth infections are one of the most common healthcare problems worldwide and they especially affect to the poorest communities in tropical and subtropical areas. Nowadays, diagnosis of intestinal parasites is performed by highly skilled medical staff, directly examining samples in the laboratory via a microscope, a laborious and time-consuming work. Automatic deep learning-based object detection methods can help to automatically detect and identify intestinal parasitic eggs, or at least reduce the workload. In this work, the application of novel Transformer-based architectures is proposed to solve the parasitic egg detection task in microscopic images. Several detection methods and backbones have been analyzed and compared, obtaining up to 0.875 mIoU score on the dataset used for testing.

Intestinal parasitic infections are a healthcare problem with a high impact in some areas. While nowadays the assessment performed by experts is mostly manual, it is possible to introduce machine learning techniques to help automating this task, or at least reduce the workload. This can lead to shorter detection times and faster treatment application. In the context of deep learning, several object detection techniques have been proposed and validated on general purpose datasets such as ImageNet or COCO. In this work, an ensemble of these is proposed for this particular task. The merged detections of FasterRCNN, TOOD, YOLOX and Cascade with Swin-Transformers applied to this problem achieved 0.915 IoU, larger than the result that each method obtained independently.

Automating the analysis of soil parameters can optimize the fertilization process, saving time and reducing the costs of food production, leading to a more sustainable agriculture. The work presented in this paper is part of the HYPERVIEW Challenge: Seeing Beyond the Visible. Several methods are proposed, based both on traditional approaches such as Support Vector Regression (SVR) and k-Nearest Neighbors (k-NN), as well as modern neural networks. A parameterized preprocessing stage has been proposed to deal with the varying size of the input data. The best results have been obtained with the k-NN model and the grid division of the images.

Abstract Fast automatic handgun detection can be very useful to avoid or mitigate risks in public spaces. Detectors based on deep learning methods have been proposed in the literature to trigger an alarm if a handgun is detected in the image. However, those detectors are solely based on the weapon appearance on the image. In this work, we propose to combine the detector with the individual’s pose information in order to improve overall performance. To this end, a model that integrates grayscale images from the output of the handgun detector and heatmap-like images that represent pose is proposed. The results show an improvement over the original handgun detector. The proposed network provides a maximum improvement of a 17.5% in AP of the proposed combinational model over the baseline handgun detector.