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The objective of the CyberScout project is to develop an autonomous surveillance and reconnaissance system. In this paper, we focus on advances in vision-based surveillance agents for detection, scene mosaicing, classification and correspondence. An agent-based software framework is used to promote synergy between the various surveillance algorithms and provide a distributed computing infrastructure for the system.

In Carnegie Mellon University’s CyberScout project, we are developing mobile and stationary sentries capable of autonomous reconnaissance and surveillance. In this paper, we describe recent advances in the areas of efficient perception algorithms (detection, classification, and correspondence) and mission planning. In detection, we have achieved improved rejection of camera jitter and environmental variations (e.g., lighting, moving foliage) through multi-modal filtering, and we have implemented panoramic backgrounding through pseudo-real-time mosaicing. In classification, we present methods for discriminating between individuals, groups of individuals, and vehicles, and between individuals with and without backpacks. In correspondence, we describe an accurate multi-hypothesis approach based on both motion and appearance. Finally, in mission planning, we describe mapbuilding using multiple sensory cues and a computationally efficient decentralized planner for multiple platforms.

In Carnegie Mellon University’s CyberScout project, we are developing mobile and stationary sentries capable of autonomous reconnaissance and surveillance. In this paper, we describe recent advances in the areas of efficient perception algorithms (detection, classification, and correspondence) and mission planning. In detection, we have achieved improved rejection of camera jitter and environmental variations (e.g., lighting, moving foliage) through multi-modal filtering, and we have implemented panoramic backgrounding through pseudo-real-time mosaicing. In classification, we present methods for discriminating between individuals, groups of individuals, and vehicles, and between individuals with and without backpacks. In correspondence, we describe an accurate multi-hypothesis approach based on both motion and appearance. Finally, in mission planning, we describe mapbuilding using multiple sensory cues and a computationally efficient decentralized planner for multiple platforms.

This paper describes our current effort to develop robotic vehicles for tactical distributed surveillance. Our research is focused on multi-agent collaboration, reconfigurable systems, efficient perception and sensor fusion, distributed command and control, and task decomposition. In particular, this paper describes the main features and capabilities of our All Terrain Vehicles (ATVs), concentrating on their autonomous navigation capabilities.

Institute for Software Research

This paper describes our current effort to develop robotic vehicles for tactical distributed surveillance. Our research is focused on multi-agent collaboration, reconfigurable systems, efficient perception and sensor fusion, distributed command and control, and task decomposition. In particular, this paper describes the main features and capabilities of our All Terrain Vehicles (ATVs), concentrating on their autonomous navigation capabilities.