The world that we live in is a complex network of agents and their interactions which are termed as events. An instance of an event is composed of directly measurable low-level actions (which I term sub-events) having a temporal order. Also, the agents can act independently (e.g. voting) as well as collectively (e.g. scoring a touch-down in a football game) to perform an event. With the dawn of the new millennium, the low-level vision tasks such as segmentation, object classification, and tracking have become fairly robust. But a representational gap still exists between low-level measurements and high-level understanding of video sequences. This dissertation is an effort to bridge that gap where I propose novel learning, detection, representation, indexing and retrieval approaches for multi-agent events in videos.; In order to achieve the goal of high-level understanding of videos, firstly, I apply statistical learning techniques to model the multiple agent events. For that purpose, I use the training videos to model the events by estimating the conditional dependencies between sub-events. Thus, given a video sequence, I track the people (heads and hand regions) and objects using a Meanshift tracker. An underlying rule-based system detects the sub-events using the tracked trajectories of the people and objects, based on their relative motion. Next, an event model is constructed by estimating the sub-event dependencies, that is, how frequently sub-event B occurs given that sub-event A has occurred. The advantages of such an event model are two-fold. First, I do not require prior knowledge of the number of agents involved in an event. Second, no assumptions are made about the length of an event.; Secondly, after learning the event models, I detect events in a novel video by using graph clustering techniques. To that end, I construct a graph of temporally ordered sub-events occurring in the novel video. Next, using the learnt event model, I estimate a weight matrix of conditional dependencies between sub-events in the novel video. Further application of Normalized Cut (graph clustering technique) on the estimated weight matrix facilitate in detecting events in the novel video. The principal assumption made in this work is that the events are composed of highly correlated chains of sub-events that have high conditional dependency (association) within the cluster and relatively low conditional dependency (disassociation) between clusters.; Thirdly, in order to represent the detected events, I propose an extension of CASE representation of natural languages. I extend CASE to allow the representation of temporal structure between sub-events. Also, in order to capture both multi-agent and multi-threaded events, I introduce a hierarchical CASE representation of events in terms of sub-events and case-lists. The essence of the proposition is that, based on the temporal relationships of the agent motions and a description of its state, it is possible to build a formal description of an event. Furthermore, I recognize the importance of representing the variations in the temporal order of sub-events, that may occur in an event, and encode the temporal probabilities directly into my event representation. The proposed extended representation with probabilistic temporal encoding is termed P-CASE that allows a plausible means of interface between users and the computer. Using the P-CASE representation I automatically encode the event ontology from training videos. This offers a significant advantage, since the domain experts do not have to go through the tedious task of determining the structure of events by browsing all the videos.; Finally, I utilize the event representation for indexing and retrieval of events. Given the different instances of a particular event, I index the events using the P-CASE representation. Next, given a query in the P-CASE representation, event retrieval is performed using a two-level search. At the first level, a m
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