James Petterson

Large-Scale Vehicle Detection, Indexing, and Search in Urban Surveillance Videos

We present a novel approach for visual detection and attribute-based search of vehicles in crowded surveillance scenes. Large-scale processing is addressed along two dimensions: 1) large-scale indexing, where hundreds of billions of events need to be archived per month to enable effective search and 2) learning vehicle detectors with large-scale feature selection, using a feature pool containing millions of feature descriptors. Our method for vehicle detection also explicitly models occlusions and multiple vehicle types (e.g., buses, trucks, SUVs, cars), while requiring very few manual labeling. It runs quite efficiently at an average of 66 Hz on a conventional laptop computer. Once a vehicle is detected and tracked over the video, fine-grained attributes are extracted and ingested into a database to allow future search queries such as “Show me all blue trucks larger than 7 ft. length traveling at high speed northbound last Saturday, from 2 pm to 5 pm”. We perform a comprehensive quantitative analysis to validate our approach, showing its usefulness in realistic urban surveillance settings.

Large-scale vehicle detection in challenging urban surveillance environments

We present a novel approach for vehicle detection in urban surveillance videos, capable of handling unstructured and crowded environments with large occlusions, different vehicle shapes, and environmental conditions such as lighting changes, rain, shadows, and reflections. This is achieved with virtually no manual labeling efforts. The system runs quite efficiently at an average of 66Hz on a conventional laptop computer. Our proposed approach relies on three key contributions: 1) a co-training scheme where data is automatically captured based on motion and shape cues and used to train a detector based on appearance information; 2) an occlusion handling technique based on synthetically generated training samples obtained through Poisson image reconstruction from image gradients; 3) massively parallel feature selection over multiple feature planes which allows the final detector to be more accurate and more efficient. We perform a comprehensive quantitative analysis to validate our approach, showing its usefulness in realistic urban surveillance settings.