Early video smoke detection system to improve fire protection in rolling stocks

Abstract

This paper presents a video system, operating in the visible spectrum range, for early smoke detection in passenger trains. The main idea is integrating standard smoke sensors with the results of a smoke detection processing, which exploits video surveillance cameras already available on-board the train. To this aim a novel video processing flow is proposed exploiting temporal, spatial and chromatic characteristics of the reference scenario. The proposed algorithm has been successfully verified with several video set and its implementation complexity fully characterize

AdViSED: Advanced Video SmokE Detection for Real-Time Measurements in Antifire Indoor and Outdoor Systems

“…Techniques such as in [18,28], based on CNNs, are difficult to implement in real scenarios due to the lack of a large dataset for the training videos. This work also outperforms a preliminary algorithm, studied and presented by the authors in [22,23], which was using SAD (sum of absolute differences) block matching as motion estimation, plus other image processing tasks, such as morphological filter, bounding boxes extraction, features extraction, correct bounding boxes selection, and space and time analysis.…”

“…Instead, for wildfire in forests or rural environments [13], which is out of the scope of this work, other techniques must be used, optimized for scenes observed at distances of several km. Many studies have been recently proposed in literature for video smoke detection [14][15][16][17][18][19][20][21][22][23][24][25].…”

“…After color analysis, image segmentation, blob labeling, features analysis, and M out of N decisor, an alarm signal is generated. When comparing the new algorithm with that in [22,23], it can be observed that there is an increase of all the evaluation metrics, such as recall, accuracy, F1 score, precision, and MCC (Matthews correlation coefficient) and a reduction of the algorithm implementation complexity that allows implementing the new measuring system in real-time and with low power. Figure 1 in a computing platform, the tasks motion-detection/color-segmentation and the tasks time/edge-based analysis can be parallelized, while the rest of the functions have to be calculated according to a sequential flow.…”

Real-time image and video processing for advanced services on-board vehicles for passenger transport

“…To be noted that using data fusion, and re-using the same videocamera infrastructure for multiple additional services for passenger public transport, such as fire alarm, smoke alarm, people counting, and so on, is missing in other works at the state-of-the-art. For example the work in [19], considered just fire alarm generation in rolling stocks, while using video sensors multiple sensors can be implemented. Instead, using as in [2] only 1 camera the achieved rate of missed detections or false alarms in not acceptable for a fully autonomous system, since the accuracy is 75%, and the intervention of the staff crew is required.…”

“…Multiple services Fusion of multiple views [19] No, only fire alarm No [2] Yes, fire alarms, people counting, driver drowsiness detection No This work Yes…”