Recognition of human interactions with vehicles using 3-D models and dynamic context

textThis dissertation describes two distinctive methods for human-vehicle interaction recognition: one for ground level videos and the other for aerial videos. For ground level videos, this dissertation presents a novel methodology which is able to estimate a detailed status of a scene involving multiple humans and vehicles. The system tracks their configuration even when they are performing complex interactions with severe occlusion such as when four persons are exiting a car together. The motivation is to identify the 3-D states of vehicles (e.g. status of doors), their relations with persons, which is necessary to analyze complex human-vehicle interactions (e.g. breaking into or stealing a vehicle), and the motion of humans and car doors to detect atomic human-vehicle interactions. A probabilistic algorithm has been designed to track humans and analyze their dynamic relationships with vehicles using a dynamic context. We have focused on two ideas. One is that many simple events can be detected based on a low-level analysis, and these detected events must contextually meet with human/vehicle status tracking results. The other is that the motion clue interferes with states in the current and future frames, and analyzing the motion is critical to detect such simple events. Our approach updates the probability of a person (or a vehicle) having a particular state based on these basic observed events. The probabilistic inference is made for the tracking process to match event-based evidence and motion-based evidence. For aerial videos, the object resolution is low, the visual cues are vague, and the detection and tracking of objects is less reliable as a consequence. Any method that requires accurate tracking of objects or the exact matching of event definition are better avoided. To address these issues, we present a temporal logic based approach which does not require training from event examples. At the low-level, we employ dynamic programming to perform fast model fitting between the tracked vehicle and the rendered 3-D vehicle models. At the semantic-level, given the localized event region of interest (ROI), we verify the time series of human-vehicle relationships with the pre-specified event definitions in a piecewise fashion. With special interest in recognizing a person getting into and out of a vehicle, we have tested our method on a subset of the VIRAT Aerial Video dataset and achieved superior results.Electrical and Computer Engineerin

Smart environment monitoring through micro unmanned aerial vehicles

In recent years, the improvements of small-scale Unmanned Aerial Vehicles (UAVs) in terms of flight time, automatic control, and remote transmission are promoting the development of a wide range of practical applications. In aerial video surveillance, the monitoring of broad areas still has many challenges due to the achievement of different tasks in real-time, including mosaicking, change detection, and object detection. In this thesis work, a small-scale UAV based vision system to maintain regular surveillance over target areas is proposed. The system works in two modes. The first mode allows to monitor an area of interest by performing several flights. During the first flight, it creates an incremental geo-referenced mosaic of an area of interest and classifies all the known elements (e.g., persons) found on the ground by an improved Faster R-CNN architecture previously trained. In subsequent reconnaissance flights, the system searches for any changes (e.g., disappearance of persons) that may occur in the mosaic by a histogram equalization and RGB-Local Binary Pattern (RGB-LBP) based algorithm. If present, the mosaic is updated. The second mode, allows to perform a real-time classification by using, again, our improved Faster R-CNN model, useful for time-critical operations. Thanks to different design features, the system works in real-time and performs mosaicking and change detection tasks at low-altitude, thus allowing the classification even of small objects. The proposed system was tested by using the whole set of challenging video sequences contained in the UAV Mosaicking and Change Detection (UMCD) dataset and other public datasets. The evaluation of the system by well-known performance metrics has shown remarkable results in terms of mosaic creation and updating, as well as in terms of change detection and object detection