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Asaad Hakeem

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AIJ Journal 2007 Journal Article

Learning, detection and representation of multi-agent events in videos

Asaad Hakeem
Mubarak Shah

In this paper, we model multi-agent events in terms of a temporally varying sequence of sub-events, and propose a novel approach for learning, detecting and representing events in videos. The proposed approach has three main steps. First, in order to learn the event structure from training videos, we automatically encode the sub-event dependency graph, which is the learnt event model that depicts the conditional dependency between sub-events. Second, we pose the problem of event detection in novel videos as clustering the maximally correlated sub-events using normalized cuts. The principal assumption made in this work is that the events are composed of a highly correlated chain of sub-events that have high weights (association) within the cluster and relatively low weights (disassociation) between the clusters. The event detection does not require prior knowledge of the number of agents involved in an event and does not make any assumptions about the length of an event. Third, we recognize the fact that any abstract event model should extend to representations related to human understanding of events. Therefore, we propose an extension of CASE representation of natural languages that allows a plausible means of interface between users and the computer. We show results of learning, detection, and representation of events for videos in the meeting, surveillance, and railroad monitoring domains.

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AAAI Conference 2005 Conference Paper

Multiple Agent Event Detection and Representation in Videos

Asaad Hakeem

We propose a novel method to detect events involving multiple agents in a video and to learn their structure in terms of temporally related chain of sub-events. The proposed method has three significant contributions over existing frameworks. First, in order to learn the event structure from training videos, we present the concept of a video event graph, which is composed of temporally related sub-events. Using the video event graph, we automatically encode the event dependency graph. The event dependency graph is the learnt event model that depicts the frequency of occurrence of conditionally dependent sub-events. Second, we pose the problem of event detection in novel videos as clustering the maximally correlated sub-events, and use normalized cuts to determine these clusters. The principal assumption made in this work is that the events are composed of highly correlated chain of sub-events, that have high weights (association) within the cluster and relatively low weights (disassociation) between clusters. These weights (between sub-events) are the likelihood estimates obtained from the event models. Last, we recognize the importance of representing the variations in the temporal order of sub-events, occurring in an event, and encode the probabilities directly into our representation. We show results of our learning, detection, and representation of events for videos in the meeting, surveillance, and railroad monitoring domains.

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AAAI Conference 2004 Conference Paper

CASEE: A Hierarchical Event Representation for the Analysis of Videos

Asaad Hakeem

A representational gap exists between low-level measurements (segmentation, object classification, tracking) and high-level understanding of video sequences. In this paper, we propose a novel representation of events in videos to bridge this gap, based on the CASE representation of natural languages. The proposed representation has three significant contributions over existing frameworks. First, we recognize the importance of causal and temporal relationships between sub-events and extend CASE to allow the representation of temporal structure and causality between sub-events. Second, in order to capture both multi-agent and multi-threaded events, we introduce a hierarchical CASE representation of events in terms of sub-events and case-lists. Last, for purposes of implementation we present the concept of a temporal event-tree, and pose the problem of event detection as subtree pattern matching. By extending CASE, a natural language representation, for the representation of events, the proposed work allows a plausible means of interface between users and the computer. We show two important applications of the proposed event representation for the automated annotation of standard meeting video sequences, and for event detection in extended videos of railroad crossings.

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