Here we investigate the automatic detection of fire pixel regions in conventional video (or still) imagery within realtime bounds. As an extension to prior, established approaches within this field we specifically look to extend the primary use of threshold-driven colour spectroscopy to the combined use of colour-texture feature descriptors as an input to a trained classification approach that is independent of temporal information. We show the limitations of such spectroscopy driven approaches on simple, real-world examples and propose our novel extension as a robust, real-time solution within this field by combining simple texture descriptors to illustrate maximal 98% fire region detection. Index Terms-fire detection, texture, real-time, nontemporal A number of factors have driven forward the increased need for fire (or flame) detection within video sequences for deployment in a wide variety of automatic monitoring tasks. The increasing prevalence of industrial, public space and general environment monitoring using security-driven CCTV video systems has given rise to the consideration of these systems as secondary sources of both initial fire detection (in addition to traditional smoke/heat based systems). Furthermore, the on-going consideration of remote vehicles for fire detection and monitoring tasks [1, 2] adds further to the demand for autonomous fire detection from such platforms themselves move towards autonomous navigation and tasking [3] . In the latter case, attention turns not only to the detection of fire itself but also its internal geography of the fire and temporal development [4] . Prior work in area concentrates itself either on the use of a purely colour based approach [5, 6, 7, 8, 9 , 4] or a combination of colour and high-order temporal information [10, 11, 12, 13] . Early work emanated from the colour-threshold approach of [5] which was extended with the basic consideration of motion by [10] . Later work considered the temporal variation (flame flicker) of fire imagery within the Fourier domain [11] with further studies formulating a Hidden Markov Model problem [12] . More recently work considering the temporal aspect of the problem has investigated time-derivatives over the image [13] . Although flame flicker is generally not sinusoidal or periodic under all conditions, a frequency of 10Hz has been observed in generalised observational studies [14] . As such, [15] considered the use of the wavelet transform as a temporal feature. In later applications [7] we still see the basic approaches of [10] underlying colour-driven approaches although more sophisticated colour models based on a derivative of background segmentation [9] and consideration of alternative colour spaces [8] are proposed. In general recent works report~98-99% detection rates with frame rates in the region of 10-40 fps on relatively small image sizes (CIF or similar) [9, 8] . Notably work on the application of machine-learning type classification approaches to the fire detection problem is limited with the work of [16] considering a colour-driven approach utilising temporal shape features as an input to a neural network and similarly the work of [17] utilising wavelet coefficients as an input to an SVM type classifier. The majority of early work purely considers colour-based classification within RGB colour-space [5, 10] with these approaches seeing further merit in later work [13, 15, 4] . Further refinements considered alternative chromaticity, HSV or YCrCb colourspace formulations [6, 8, 17] . Here, by contrast to previous classifier-driven work [16, 17, 4] , we consider a non-temporal classification model for fire regions within the image based on a novel combination of HSV colour and statistical texture features [18] . We show that comparable detection results are achievable using similar classification approaches to [16, 17] without consideration of temporal information making the proposed approach applicable to still or video imagery alike. As previously discussed, prior work on the automatic detection of fire within imagery centres around the consideration of colour, movement (or scene change) and short-order temporal characteristics [12, 15, 17, 16] . Although, an increasing range of generalized object recognition approaches are becoming prevalent for objects of varying visual complexity [19] , the target deployment of an automatic vision-based fire-detection system has an integral real-time requirement. To this end, it is the simpler detection strategies that are achievable within real-time bounds that come to prominence. Here we investigate the strength of simple, non-temporal colour spectroscopy based approaches [5, 10] , illustrate their weaknesses (Section 4.1) and propose an combined texture and colour based feature descriptor as an input to a trained classifier based detection/decision boundary (Section 4.2) . In a similar vein to the prior work of [6, 17] we utilise a perceptual colour-space, namely HSV (Hue, Saturation, Vari-
Chenebert et al. REF use a combined texture and color based feature descriptor as an input to a trained classifier based detection boundary.
A non-temporal texture driven approach to real-time fire detection
2011 18th IEEE International Conference on Image Processing
2011 18th IEEE International Conference on Image Processing
Computer Science