Many visual recognition problems can be approached by counting instances. To determine whether an event is present in a long internet video, one could count how many frames seem to contain the activity. Classifying the activity of a group of people can be done by counting the actions of individual people. Encoding these cardinality relationships can reduce sensitivity to clutter, in the form of irrelevant frames or individuals not involved in a group activity. Learned parameters can encode how many instances tend to occur in a class of interest. To this end, this paper develops a powerful and flexible framework to infer any cardinality relation between latent labels in a multi-instance model. Hard or soft cardinality relations can be encoded to tackle diverse levels of ambiguity. Experiments on tasks such as human activity recognition, video event detection, and video summarization demonstrate the effectiveness of using cardinality relations for improving recognition results.
This paper presents a novel model for cardinality relations in visual recognition, in particular for the analysis of video sequences. Existing video analysis methods generally focus on structured spatio-temporal models, complementary to our proposed approach. For instance, pioneering work was done by @cite_6 in analyzing structured videos by creating storyline'' models populated from AND-OR graph representations. Related models have proven effective at analyzing scenes of human activity more broadly in work by @cite_2 . A series of recent papers has focused on the problem of group activity recognition, inferring an activity that is performed by a set of people in a scene. @cite_40 @cite_23 , @cite_34 , and @cite_0 devised models for spatial and temporal relations between the individuals involved in a putative interaction. @cite_36 consider contextual relations between humans and objects in a scene to detect interactions of interest. The structural relations exploited by these methods are a key component of activity understanding, but present different information from the cardinality relations we study.
@cite_36 @cite_6 @cite_0 @cite_40 @cite_23 @cite_2 @cite_34
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In this paper, we present a 4D human-object interaction (4DHOI) model for solving three vision tasks jointly: i) event segmentation from a video sequence, ii) event recognition and parsing, and iii) contextual object localization. The 4DHOI model represents the geometric, temporal, and semantic relations in daily events involving human-object interactions. In 3D space, the interactions of human poses and contextual objects are modeled by semantic co-occurrence and geometric compatibility. On the time axis, the interactions are represented as a sequence of atomic event transitions with coherent objects. The 4DHOI model is a hierarchical spatial-temporal graph representation which can be used for inferring scene functionality and object affordance. The graph structures and parameters are learned using an ordered expectation maximization algorithm which mines the spatial-temporal structures of events from RGB-D video samples. Given an input RGB-D video, the inference is performed by a dynamic programming beam search algorithm which simultaneously carries out event segmentation, recognition, and object localization. We collected a large multiview RGB-D event dataset which contains 3,815 video sequences and 383,036 RGB-D frames captured by three RGB-D cameras. The experimental results on three challenging datasets demonstrate the strength of the proposed method. We focus on modeling human activities comprising multiple actions in a completely unsupervised setting. Our model learns the high-level action co-occurrence and temporal relations between the actions in the activity video. We consider the video as a sequence of short-term action clips, called action-words, and an activity is about a set of action-topics indicating which actions are present in the video. Then we propose a new probabilistic model relating the action-words and the action-topics. It allows us to model long-range action relations that commonly exist in the complex activity, which is challenging to capture in the previous works. We apply our model to unsupervised action segmentation and recognition, and also to a novel application that detects forgotten actions, which we call action patching. For evaluation, we also contribute a new challenging RGB-D activity video dataset recorded by the new Kinect v2, which contains several human daily activities as compositions of multiple actions interacted with different objects. The extensive experiments show the effectiveness of our model. Analyzing videos of human activities involves not only recognizing actions (typically based on their appearances), but also determining the story plot of the video. The storyline of a video describes causal relationships between actions. Beyond recognition of individual actions, discovering causal relationships helps to better understand the semantic meaning of the activities. We present an approach to learn a visually grounded storyline model of videos directly from weakly labeled data. The storyline model is represented as an AND-OR graph, a structure that can compactly encode storyline variation across videos. The edges in the AND-OR graph correspond to causal relationships which are represented in terms of spatio-temporal constraints. We formulate an Integer Programming framework for action recognition and storyline extraction using the storyline model and visual groundings learned from training data. In this paper, we study the problem of social relational inference using visual concepts which serve as indicators of actors' social interactions. While social network analysis from videos has started to gain attention in the recent years, the existing work either uses proximity or co-occurrence statistics, or exploit a holistic model of the scene content where the relations are assumed to stay constant throughout the video. This work permits changing relations and argues that there exists a relationship between the visual concepts and the social relations among actors, which is a fundamentally new concept in computer vision. Specifically, we leverage the existing large-scale concept detectors to generate concept score vectors to represent the video content, and we further map them to grouping cues that are used to detect the social structure. In our framework, a probabilistic graphical model with temporal smoothing provides a means to analyze social relations among actors and detect communities. Experiments on Youtube videos and theatrical movies validate the proposed framework.