Fire Detection Algorithms Using Multimodal ... - Bilkent University
Fire Detection Algorithms Using Multimodal ... - Bilkent University Fire Detection Algorithms Using Multimodal ... - Bilkent University
CHAPTER 6. WILDFIRE DETECTION 83Figure 6.1: Snapshot of a typical wildfire smoke captured by a forest watch towerwhich is 5 km away from the fire (rising smoke is marked with an arrow).by Hubert L. Dreyfus as opposed to ‘generalized’ AI. According to this frameworkeach specific problem in AI should be addressed as an individual engineeringproblem with its own characteristics [25], [26].The proposed automatic video based wildfire detection algorithm is based onfour sub-algorithms: (i) slow moving video object detection, (ii) smoke-coloredregion detection, (iii) rising video object detection, (iv) shadow detection andelimination. Each sub-algorithm decides on the existence of smoke in the viewingrange of the camera separately. Decisions from sub-algorithms are combined togetherby an adaptive active fusion method. Initial weights of the sub-algorithms
CHAPTER 6. WILDFIRE DETECTION 84are determined from actual forest fire videos and test fires. They are updated usingthe least-mean-square (LMS) algorithm during initial installation. The LMSalgorithm was initially developed by Widrow in 1960’s together with neural networks[91]. The error function in the LMS adaptation is defined as the differencebetween the overall decision of the compound algorithm and the decision of anoracle. In our case, the oracle is the security guard. The compound decision algorithmwill obviously produce false alarms. The system asks the guard to verifyits decision whenever an alarm occurs. In this way, the user actively participatein the learning process.The chapter is organized as follows: Section 6.2 describes briefly each oneof the four sub-algorithms which make up the compound (main) wildfire detectionalgorithm. Adaptive active fusion method is described in Section 6.3. InSection 6.4, experimental results are presented and the proposed online activefusion method is compared with the universal linear predictor and the weightedmajority algorithms. Finally, conclusions are drawn in Section 6.5.6.2 Building Blocks of Wildfire Detection AlgorithmWildfire detection algorithm is developed to recognize the existence of wildfiresmoke within the viewing range of the camera monitoring forestal areas. The proposedwildfire smoke detection algorithm consists of four main sub-algorithms:(i) slow moving object detection in video, (ii) smoke-colored region detection,(iii) rising video object detection, (iv) shadow detection and elimination of shadowregions, with decision functions, D 1 (x, n), D 2 (x, n), D 3 (x, n) and D 4 (x, n), respectively,for each pixel at location x of every incoming image frame at timestep n. Computationally efficient sub-algorithms are selected in order to realizea real-time wildfire detection system working in a standard PC.Decision functions D i , i = 1, ..., M of sub-algorithms do not produce binaryvalues 1 (correct) or −1 (false), but they produce zero-mean real numbers for
- Page 51 and 52: CHAPTER 2. FLAME DETECTION IN VISIB
- Page 53 and 54: CHAPTER 2. FLAME DETECTION IN VISIB
- Page 55 and 56: CHAPTER 3. FLAME DETECTION IN INFRA
- Page 57 and 58: CHAPTER 3. FLAME DETECTION IN INFRA
- Page 59 and 60: CHAPTER 3. FLAME DETECTION IN INFRA
- Page 61 and 62: CHAPTER 3. FLAME DETECTION IN INFRA
- Page 63 and 64: CHAPTER 3. FLAME DETECTION IN INFRA
- Page 65 and 66: CHAPTER 3. FLAME DETECTION IN INFRA
- Page 67 and 68: CHAPTER 3. FLAME DETECTION IN INFRA
- Page 69 and 70: CHAPTER 3. FLAME DETECTION IN INFRA
- Page 71 and 72: CHAPTER 3. FLAME DETECTION IN INFRA
- Page 73 and 74: Chapter 4Short Range Smoke Detectio
- Page 75 and 76: CHAPTER 4. SHORT RANGE SMOKE DETECT
- Page 77 and 78: CHAPTER 4. SHORT RANGE SMOKE DETECT
- Page 79 and 80: CHAPTER 4. SHORT RANGE SMOKE DETECT
- Page 81 and 82: CHAPTER 4. SHORT RANGE SMOKE DETECT
- Page 83 and 84: CHAPTER 4. SHORT RANGE SMOKE DETECT
- Page 85 and 86: CHAPTER 4. SHORT RANGE SMOKE DETECT
- Page 87 and 88: Chapter 5Flame Detection Using PIRS
- Page 89 and 90: CHAPTER 5. FLAME DETECTION USING PI
- Page 91 and 92: CHAPTER 5. FLAME DETECTION USING PI
- Page 93 and 94: CHAPTER 5. FLAME DETECTION USING PI
- Page 95 and 96: CHAPTER 5. FLAME DETECTION USING PI
- Page 97 and 98: CHAPTER 5. FLAME DETECTION USING PI
- Page 99 and 100: CHAPTER 5. FLAME DETECTION USING PI
- Page 101: CHAPTER 6. WILDFIRE DETECTION 82In
- Page 105 and 106: CHAPTER 6. WILDFIRE DETECTION 86D 1
- Page 107 and 108: CHAPTER 6. WILDFIRE DETECTION 88tha
- Page 109 and 110: CHAPTER 6. WILDFIRE DETECTION 90The
- Page 111 and 112: CHAPTER 6. WILDFIRE DETECTION 92D 1
- Page 113 and 114: CHAPTER 6. WILDFIRE DETECTION 94as
- Page 115 and 116: CHAPTER 6. WILDFIRE DETECTION 96Sol
- Page 117 and 118: CHAPTER 6. WILDFIRE DETECTION 98as
- Page 119 and 120: CHAPTER 6. WILDFIRE DETECTION 100Un
- Page 121 and 122: CHAPTER 6. WILDFIRE DETECTION 102is
- Page 123 and 124: CHAPTER 6. WILDFIRE DETECTION 104Ta
- Page 125 and 126: Chapter 7Conclusion and Future Work
- Page 127 and 128: CHAPTER 7. CONCLUSION AND FUTURE WO
- Page 129 and 130: Bibliography[1] B. Albers and A. Ag
- Page 131 and 132: BIBLIOGRAPHY 112[18] D. Chetverikov
- Page 133 and 134: BIBLIOGRAPHY 114[38] G. Healey, D.
- Page 135 and 136: BIBLIOGRAPHY 116[56] U. Niesen, D.
- Page 137 and 138: BIBLIOGRAPHY 118[77] C. Stauffer an
CHAPTER 6. WILDFIRE DETECTION 83Figure 6.1: Snapshot of a typical wildfire smoke captured by a forest watch towerwhich is 5 km away from the fire (rising smoke is marked with an arrow).by Hubert L. Dreyfus as opposed to ‘generalized’ AI. According to this frameworkeach specific problem in AI should be addressed as an individual engineeringproblem with its own characteristics [25], [26].The proposed automatic video based wildfire detection algorithm is based onfour sub-algorithms: (i) slow moving video object detection, (ii) smoke-coloredregion detection, (iii) rising video object detection, (iv) shadow detection andelimination. Each sub-algorithm decides on the existence of smoke in the viewingrange of the camera separately. Decisions from sub-algorithms are combined togetherby an adaptive active fusion method. Initial weights of the sub-algorithms
Change language