In this research work, an approach for fire detection and estimation robots is presented. The approach is based on type-2 fuzzy logic system that utilizes measured temperature and light intensity to detect fires of various intensities at different distances. Type-2 fuzzy logic system (T2 FLS) is known for not needing exact mathematic model and for its capability to handle more complicated uncertain situations compared with Type-1 fuzzy logic system (T1 FLS). Due to lack of expertise for new facilities, a new approach for training experts’ expertise and setting up T2 FLS parameters from pure data is discussed in this thesis. Performance of both T1 FLS and T2 FLS regarding to same fire detection scenario are investigated and compared in this thesis. Simulation works have been done for fire detection robot of both free space scenario and new facility scenario to illustrate the operation and performance of proposed type-2 fuzzy logic system. Experiments are also performed using LEGO MINDSTROMS NXT robot to test the reliability and feasibility of the algorithm in physical environment with simple and complex situation.

Advances in social networks and multimedia technologies greatly facilitate the recording and sharing of video data on violent social and/or political events via In- ternet. These video data are a rich source of information in terms of identifying the individuals responsible for damaging public and private property through vio- lent behavior. Any abnormal, violent individual behavior could trigger a cascade of undesirable events, such as vandalism and damage to stores and public facilities. When such incidents occur, investigators usually need to analyze thousands of hours of videos recorded using handheld devices in order to identify suspects. The exhaus- tive manual investigation of these video data is highly time and resource-consuming. Automated detection techniques of abnormal events and actions based on computer vision would o↵er a more e cient solution to this problem. The first contribution described in this thesis consists of a novel method for fire detection in riot videos acquired with handheld cameras and smart-phones. This is a typical example of computer vision in the wild, where we have no control over the data acquisition process, and where the quality of the video data varies considerably. The proposed spatial model is based on the Mixtures of Gaussians model and exploits color adjacency in the visible spectrum of incandescence. The experimental results demonstrate that using this spatial model in concert with motion cues leads to highly accurate results for fire detection in noisy, complex scenes of rioting crowds. The second contribution consists in a method for detecting abnormal, violent actions that are performed by individual subjects and witnessed by passive crowds. The problem of abnormal individual behavior, such as a fight, witnessed by passive bystanders gathered into a crowd has not been studied before. We show that the presence of a passive, standing crowd is an important indicator that an abnormal action might occur. Thus, detecting the standing crowd improves the performance of detecting the abnormal action. The proposed method performs crowd detection first, followed by the detection of abnormal motion events. Our main theoretical contribution consists in linking crowd detection to abnormal, violent actions, as well as in defining novel sets of features that characterize static crowds and abnormal individual actions in both spatial and spatio-temporal domains. Experimental results are computed on a custom dataset, the Vancouver Riot Dataset, that we generated using amateur video footage acquired with handheld devices and uploaded on public social network sites. Our approach achieves good precision and recall values, which validates our system’s reliability of localizing the crowds and the abnormal actions. To summarize, this thesis focuses on the detection of two types of abnormal events occurring in violent street movements. The data are gathered by passive participants to these movements using handheld devices. Although our data sets are drawn from one single social movement (the Vancouver 2011 Stanley cup riot) we are confident that our approaches would generalize well and would be helpful to forensic activities performed in the context of other similar violent occasions. / Graduate

The capability to monitor bushfires on a large scale from space has long been identified as an important contribution to climate and atmospheric research as well as a tool an aid in natural hazard response. Since the work by Dozier (1981), fire monitoring from space has relied on the principles he described. His method of identifying fires within a pixel significantly larger than the fire by utilizing the different responses of the 3 μm and 11 μm channels has been applied to a number of sensors. Over the last decade a lot of work has been invested to refine and validate fire detections based on this approach. So far, the application of the method proposed by Dozier (1981) reached its peak with the launch of the MODIS instrument on board the Terra satellite. In contrast to earlier sensors, MODIS was equipped with spectral channels specifically designed for the detection of fires with algorithms based on the work by Dozier (1981). These channels were designed to overcome problems experienced with other platforms, the biggest of which is the saturation of the 3 μm channel caused by big, hot fires. Since its launch, MODIS has proven itself to be a capable platform to provide worldwide fire detection at a moderate resolution of 1 km on a daily basis. / It is the intention of this work to open up new opportunities in remote sensing of fires from satellites by showing capabilities and limitations in the application of other spectral channels, in particular the 2.1 μm channel of MODIS, than the ones currently used. This channel is chosen for investigation as fires are expected to emit a significant amount of energy in this bandwidth and as it is available at a native resolution of 500 m on MODIS; double the resolution of the 3 μm and 11 μm channels. The modelling of blackbodies of typical bushfire temperatures shows that a fire detection method based on the 2.1 μm channel will not be able to replace the current methods. Blackbodies of temperatures around 600 to 700 K, that are common for smoldering fires, do not emit a great amount of energy at 2.1 μm. It would be hardly possible to detect those fires by utilizing the 2.1 μm channel. The established methods based on the 3 μm and 11 μm channels are expected to work better in these cases. Blackbodies of typically flaming fires (above 800 K) however show a very high emission around 2.1 μm that should make their detection using the 2.1 μm channel possible. / In order to develop a fire detection method based on the 2.1 μm channel, it is necessary to differentiate between the radiance caused by a fire of sub pixel size and the radiance of a pixel caused by the reflection of sunlight. This is attempted by using time series of past observations to model a reflectance value for a given pixel expected in absence of a fire. A fire detection algorithm exploiting the difference between the expected and observed reflectance is implemented and its detection results are compared to high resolution ASTER fire maps, the standard MODIS fire detection algorithm (MOD14) and burnt area maps. The detections of the method based on the 2.1 μm channel are found to correspond very well with the other three datasets. However, the comparison showed detections that do not align with MOD14 active fire detections but are generally aligned with burn areas. This phenomena has to be investigated in the future.

The aim of this work was to use remote sensing data from the MODIS instrument of the Terra satellite to detect bush fires in Australia. This included preprocessing the demodulator output, bit synchronization and reassembly of data packets. IMAPP was used to do the geolocation and data calibration. The fire detection used a combination of fixed threshold techniques with difference tests and background comparisons. The results were projected in a rectangular latidue/longitude map to remedy the bow tie effect. Algorithms were implemented in C and Matlab. It proved to be possible to detect fires in the available data. The results were compared with fire detection done done by NASA and fire detections based on other sensors and found to be very similar. / Das Ziel dieser Arbeit war die Nutzung von Fernerkundungsdaten des MODIS Instruments an Bord des Satelliten Terra zur Erkennung von Buschfeuern in Australien. Das schloss die Vorverarbeitung der Daten vom Demodulator, die Bitsynchronisation und die Umpacketierung der Daten ein. IMAPP wurde genutzt um die Daten zu kalibrieren und zu geolokalisieren. Die Feuererkennung bedient sich einer Kombination von absoluten Schwellwerttests, Differenztests und Vergleichen mit dem Hintergrund. Die Ergebnisse wurden in eine rechteckige Laengen/Breitengradkarte projiziert um dem BowTie Effekt entgegenzuwirken. Die benutzten Algrorithmen wurden in C und Matlab implementiert. Es zeigte sich, dass es moeglich ist in den verfuegbaren Daten Feuer zu erkennen. Die Ergebnisse wurden mit Feuererkennungen der NASA und Feuererkennung die auf anderen Sensoren basieren verglichen und fuer sehr aehnlich befunden.

As our society gets increasingly mobile, it is becoming commonplace for residences to remain vacant for a significant amount of time every day. Unfortunately, emergencies can occur during those time, which may require immediate mitigatory action. This project proposes an approach that allows the resident to be notified of such emergencies and to perform mitigatory actions, even when she is hundreds of miles away. Our infrastructure includes three components: (1) programmable sensor devices to detect emergency situations; (2) a Web service hosted in the resident's home computer to send a notification to the smartphone of the user; and (3) a smartphone app that communicates with this Web service to notify the user, and provides a interface for the user to perform any mitigatory action. We develop a prototype system for detecting fire and intrusion emergencies. Our prototype system uses two sunSPOTs as sensors, an iRobot Create® as a mitigatory device, an Android app for user notification. / text

Recent developments at Fukushima Nuclear Power Plant in Japan have created urgency in the scientist community to come up with solutions for hostile industrial environment in case of a breakdown or natural disaster. There are many hazardous scenarios in an indoor industrial environment such as risk of fire, failure of high speed rotary machines, chemical leaks, etc. Fire is one of the leading causes for workplace injuries and fatalities. The current fire protection systems available in the market mainly consist of a sprinkler systems and personnel on duty. In the case of a sprinkler system there could be several things that could go wrong, such as spraying water on a fire created by an oil leak may even spread it, the water from the sprinkler system may harm the machinery in use that is not under the fire threat and the water could potentially destroy expensive raw material, finished goods and valuable electronic and printed data. There is a dire need of an inexpensive autonomous system that can detect and approach the source of these hazardous scenarios. This thesis focuses mainly on industrial fires but, using same or similar techniques on different sensors, may allow it to detect and approach other hostile situations in industrial workplace. Autonomous robots can be equipped to detect potential threats of fire and find out the source while avoiding the obstacles during navigation. The proposed system uses Modified Voting Logic Fusion to approach and declare a potential fire source autonomously. The robot follows the increasing gradient of light and heat intensity to identify the threat and approach the source.

It is difficult to capture the early signs of a forest fire at night using current visible-spectrum sensor technology. Infrared (IR) light sensors, on the other hand, can detect heat plumes expelled at the initial stages of a forest fire around the clock. Long-wave IR (LWIR) is commonly referred to as the “thermal infrared” region where thermal emissions are captured without the need of, or reflections from, external radiation sources. Mid‑wave IR (MWIR) bands lie between the “thermal infrared” and “reflected infrared” (i.e. short-wave IR) regions. Both LWIR and MWIR spectral regions are able to detect thermal radiation; however, they differ significantly in regards to their detection sensitivity of forest-fire heat plumes. Fires fueled by organic material (i.e. wood, leaves, etc.) primarily emit hot carbon dioxide (CO2) gas at combustion. Consequently, because CO2 is also present in the atmosphere, re-emission restricts the spectral transmittance and hence spectral radiance over a wide range of frequencies in the MWIR region. Moreover, as the distance between the detector and fire’s heat plume becomes greater, the additional CO2 introduced into the detection path leads to further attenuation of photon emittance. Since these absorption frequencies also lie within the response bandwidth of the MWIR sensor material, captured heat plume radiation manifests itself as a group of “flooded” or saturated pixels that exhibit very little dynamic behavior. Meanwhile, since the LWIR spectral region is not significantly affected by atmospheric gas absorption, its sensor is able to capture the forest fire’s heat plume thermal signature at far range without such complications. By exploiting the underlying spectral differences between LWIR and MWIR regions, this study aims to achieve early forest fire heat plume detection via direct identification of its dynamic characteristics whist concurrent attenuation of detected non-fire-related radiation. A land‑based, co‑located, cooled-LWIR/cooled-MWIR (CLWIR/CMWIR) detector camera is used to capture and normalize synchronized video from which sequential spatial-domain difference frames are generated. Processed frames allow for effective extraction of the heat plume’s “flickering” features, which are characteristic to the early stages of a forest fire. A multilayer perceptron (MLP) neural network classifier is trained with feature points generated from known target samples (i.e. supervised learning). Resulting detection performance is assessed via detection time, error metrics, computation time, and parameter variation. Results indicate that heat plumes expelled at the early stages of a forest fire can be identified with high sensitivity, low false alarm, and at a farther range than commercial detectors.

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