1,720,963 research outputs found
Onfire 2023 Contest: what did we learn about real time fire detection from cameras?
No full textSeveral methods for fire detection from camera streams have been proposed in recent years. While traditional techniques often emphasize recall, they frequently neglect critical factors such as minimizing false positives, ensuring timely alarm notifications and optimizing performance for devices with limited computational resources. The ONFIRE 2023 contest evaluates various approaches for detecting fire using smart cameras and establishes new evaluation metrics to measure precision, recall, notification promptness, processing speed and resource utilization. The eight participating teams received a training set that integrated all publicly available video datasets and were evaluated on a private test set. The latter includes positive samples where fire is not present at the beginning of the clip, as well as negative samples featuring mov- ing fire-like objects. In this paper, we provide an overview of the competition’s dataset and review the proposed solutions, highlighting the winning approach, the limitations of existing datasets and the evaluation metrics used. By analyzing the results of the competition, we propose possible design choices and future directions that may help to reduce the false positive rate while preserving accuracy
Improving safety through advanced obstacle detection at railway level crossings
No full textThe detection of obstacles at railway level crossings is crucial for ensuring the safety of passengers and cargo, as well as for maintaining a smooth flow of road and rail traffic. In this context, we present a deep learning-based video analytics algorithm tailored for deployment on smart cameras, able to autonomously detects the presence of individuals and vehicles at railway level crossings. To mitigate false alarms triggered by objects when the level crossing is open (thus the passage of objects is allowed), the proposed algorithm also incorporates a deep neural network to automatically determine the barrier status (open or closed). The proposed system has been evaluated on a data set of 52 videos of railway crossings we collected from Youtube, exhibiting impressive 0.98 precision and 0.88 recall. In addition, the proposed system is designed to operate directly on smart cameras and embedded devices, eliminating the need for server infrastructure or cloud connectivity
Smart visual sensors for real time weather and ground conditions recognition for agricultural robotics
No full textFire and smoke detection from videos: A literature review under a novel taxonomy
No full textThe recent development of deep learning based fire detection techniques and the availability of smart cameras able to execute these algorithms on the edge paved the way for sophisticated and efficient video-based firefighting systems. However, the limited available data to train these algorithms cast shadows on their robustness and generalization capability. In this survey, we review 153 papers published in the literature and 17 publicly available fire detection datasets with the aim of identifying application scenarios that better describe real-world fire detection challenges. In the proposed taxonomy, these are characterized by two features: i) the fire size in the framed scene that depends on several parameters, foremost the distance from the fire but also the camera optic; ii) the background activity, due to the presence of moving objects that may mislead the detector. On this basis, we analyzed the existing methods under a common scheme according to this new taxonomy and matched the solutions with the needs of specific application scenarios. Similarly, for 9 interesting video datasets acquired from cameras, we labeled 536 videos according to the proposed taxonomy and shared these annotations with the community. The aim of this fire detection review is two-fold: on one hand, we classify the existing scientific works according to the real application scenarios, determining the features that are promising in specific operative conditions; on the other hand, we provide a detailed analysis and annotation of available datasets to promote the development of more reliable validation protocols and the collection of data from missing scenarios
Video Fire Recognition Using Zero-shot Vision-language Models Guided by a Task-aware Object Detector
No full textFOCUS: Improving fire detection on videos by scenario adaptation
No full textFire detection from video is effective for most video surveillance applications. The algorithm that processes the video acquired by cameras in real time has a twofold goal: detect as many fires as possible and keep the number of false alarms low. While existing approaches obtain the first goal, they often produce many false alarms due to their inability to account for the specific characteristics of diverse application environments. This paper introduces Fire Observation and Control Using Scenarios (FOCUS), a novel configurable fire detection method designed to bridge the gap between the literature methods and the application needs by exploiting scenario-specific knowledge. FOCUS leverages scalable and configurable modules for robust fire detection, incorporating three key steps: (1) fire detection, which identifies potential fire regions using visual cues; (2) fire candidate filtering through motion analysis, to eliminate false positives by analyzing the dynamic behavior of the identified fire candidates; (3) a vision–language model, which evaluates and confirms fire alarms by correlating visual evidence with contextual knowledge. By tailoring the configuration to the scenario and integrating the advanced filtering mechanisms according to the complexity of the environment, FOCUS improves performance in all the considered application scenarios. The analysis of the results shows that the proposed approach outperforms existing methods demonstrating higher resilience on real data, which enables its usage in real-world applications
Real-time joint recognition of weather and ground surface conditions by a multi-task deep network
Full text linkClimate change and the occurrence of intense and unexpected weather events highlighted the need for real-time
weather warning systems, especially in smart roads and isolated scenarios like rural areas. In this work, we
propose to jointly recognize the weather and the ground surface conditions using existing video surveillance
systems. Previous works separately tackled these two tasks even if they are correlated to each other. We
propose a convolutional neural network with shared weights in the lower layers and two separate classification
branches on top to exploit the correlation between the tasks and, at the same time, learn diverse high-level
features for each task. Moreover, the network architecture implements attention mechanisms allowing the
classification branches to focus on diverse image regions. The method is versatile and allows us to train the
network on partially labeled data. The experimental analysis on real data demonstrate the effectiveness of
the proposed method on both tasks, confirmed by the accuracy comparison with existing methods for the
recognition of weather and ground surface conditions. The multi-task solution improves the inference speed
(50 frames per second) and reduces the required memory (less than 1 GB) with respect to a system with two
different single-task approaches; these results confirm that the proposed solution is ready for video surveillance
applications to support smart cities
FLAME: fire detection in videos combining a deep neural network with a model-based motion analysis
No full textAmong the catastrophic natural events posing hazards to human lives and infrastructures, fire is the phenomenon causing more frequent damages. Thanks to the spread of smart cameras, video fire detection is gaining more attention as a solution to monitor wide outdoor areas where no specific sensors for smoke detection are available. However, state-of-the-art fire detectors assure a satisfactory Recall but exhibit a high false-positive rate that renders the application practically unusable. In this paper, we propose FLAME, an efficient and adaptive classification framework to address fire detection from videos. The framework integrates a state-of-the-art deep neural network for frame-wise object detection, in an automatic video analysis tool. The advantages of our approach are twofold. On the one side, we exploit advances in image detector technology to ensure a high Recall. On the other side, we design a model-based motion analysis that improves the system’s Precision by filtering out fire candidates occurring in the scene’s background or whose movements differ from those of the fire. The proposed technique, able to be executed in real-time on embedded systems, has proven to surpass the methods considered for comparison on a recent literature dataset representing several scenarios. The code and the dataset used for designing the system have been made publicly available by the authors at (https://mivia.unisa.it/large-fire-dataset-with-negative-samples-lfdn/)
An Experimental Evaluation of Smart Sensors for Pedestrian Attribute Recognition Using Multi-Task Learning and Vision Language Models
No full textThis paper presents the experimental evaluation and analyzes the results of the first edition of the pedestrian attribute recognition (PAR) contest, the international competition which focused on smart visual sensors based on multi-task computer vision methods for the recognition of binary and multi-class pedestrian attributes from images. The participant teams designed intelligent sensors based on vision-language models, transformers and convolutional neural networks that address the multi-label recognition problem leveraging task interdependencies to enhance model efficiency and effectiveness. Participants were provided with the MIVIA PAR Dataset, containing 105,244 annotated pedestrian images for training and validation, and their methods were evaluated on a private test set of over 20,000 images. In the paper, we analyze the smart visual sensors proposed by the participating teams, examining the results in terms of accuracy, standard deviation and confusion matrices and highlighting the correlations between design choices and performance. The results of this experimental evaluation, conducted in a challenging and realistic framework, suggest possible directions for future improvements in these smart sensors that are thoroughly discussed in the paper
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