SVCL Motion Classification

Sidewalk 01 ... Scene Description - [html] [pdf]
Congestion Level Experiments Oblique Angle Still				Experiment Accuracy
				Martin NN = 84% Nearest-neighbor classifier using Martin distance. State KL NN = 83% Nearest-neighbor classifier using state KL divergence State KL SVM = 89% SVM classifier using state KL kernel Image KL NN = 67% Nearest-neighbor classifier using image KL divergence Image KL SVM = 87 % SVM classifier using image KL kernel
Description In this experiment, we attempt to discriminate between three congestion levels of pedsetrian traffic using still clips at an oblique angle. Location: Just Southwest of Geisel Library, UC San Diego.	Clips: High Flow divx mov mp4 divx 1 2 3 4 mov (h.264) 1 2 3 4 mp4 (mpeg4) 1 2 3 4	Clips: Medium Flow divx mov mp4 divx 1 mov (h.264) 1 mp4 (mpeg4) 1	Clips: Low Flow divx mov mp4 divx 1 mov (h.264) 1 mp4 (mpeg4) 1

Front Angle Still				Experiment Accuracy
				Martin NN = % Nearest-neighbor classifier using Martin distance. State KL NN = % Nearest-neighbor classifier using state KL divergence State KL SVM = % SVM classifier using state KL kernel Image KL NN = % Nearest-neighbor classifier using image KL divergence Image KL SVM = % SVM classifier using image KL kernel
Description In this experiment, we attempt to discriminate between three congestion levels of pedsetrian traffic using still clips at a front angle. Location: Just Southwest of Geisel Library, UC San Diego.	Clips: High Flow divx mov mp4 divx 1 mov (h.264) 1 mp4 (mpeg4) 1	Clips: Medium Flow divx mov mp4 divx 1 2 3 mov (h.264) 1 2 3 mp4 (mpeg4) 1 2 3	Clips: Low Flow divx mov mp4 divx 1 mov (h.264) 1 mp4 (mpeg4) 1

Front Angle #2 Still				Experiment Accuracy
				Martin NN = % Nearest-neighbor classifier using Martin distance. State KL NN = % Nearest-neighbor classifier using state KL divergence State KL SVM = % SVM classifier using state KL kernel Image KL NN = % Nearest-neighbor classifier using image KL divergence Image KL SVM = % SVM classifier using image KL kernel
Description In this experiment, we attempt to discriminate between three congestion levels of pedsetrian traffic using still clips at a front angle. Location: Just Southwest of Geisel Library, UC San Diego.	Clips: High Flow divx mov mp4 divx 1 mov (h.264) 1 mp4 (mpeg4) 1	Clips: Medium Flow divx mov mp4 divx 1 2 3 mov (h.264) 1 2 3 mp4 (mpeg4) 1 2 3	Clips: Low Flow divx mov mp4 divx 1 mov (h.264) 1 mp4 (mpeg4) 1

Side Angle Still			Need other classes to discriminate.	Experiment Accuracy
				No Experimental Results
Description In this experiment, we attempt to discriminate between three congestion levels of pedsetrian traffic using still clips at a side angle. Location: Just Southwest of Geisel Library, UC San Diego.	Clips: Medium Flow divx mov mp4 divx 1 2 3 4 mov (h.264) 1 2 3 4 mp4 (mpeg4) 1 2 3 4	Clips: Low Flow divx mov mp4 divx 1 2 mov (h.264) 1 2 mp4 (mpeg4) 1 2

Side Angle #2 Still		Need other classes to discriminate.	Need other classes to discriminate.	Experiment Accuracy
				No Experimental Results
Description In this experiment, we attempt to discriminate between three congestion levels of pedsetrian traffic using still clips at a side angle. Location: Just Southwest of Geisel Library, UC San Diego.	Clips: Low Flow divx mov mp4 divx 1 mov (h.264) 1 mp4 (mpeg4) 1

Front Walking		Need other classes to discriminate.	Need other classes to discriminate.	Experiment Accuracy
				No Experimental Results
Description No experiment devised. Location: Just Southwest of Geisel Library, UC San Diego.	Clips: Low Flow divx mov mp4 divx 1 2 mov (h.264) 1 2 mp4 (mpeg4) 1 2

Front #2 Walking		Need other classes to discriminate.	Need other classes to discriminate.	Experiment Accuracy
				No Experimental Results
Description No experiment devised. Location: Just Southwest of Geisel Library, UC San Diego.	Clips: Low Flow divx mov mp4 divx 1 2 mov (h.264) 1 2 mp4 (mpeg4) 1 2