in Difficult Scenarios by Combining Motion and Appearance 

Reliable human detection is a key issue in automated visual surveillance systems. Motion detection provides a strong cue towards accomplishing this task. However, typical scenarios usually contain motion clutter leading to false alarms. MUSCLE researchers at ACV developed a real-time framework which combines a model-based human detection approach relying on motion detection and statistical learning in order to validate detected objects and remove spurious observations. The combined detection scheme shows video improvements in terms of lower false alarm rates and improved tracking performance for difficult scenarios containing moving shadows and vehicles.