We propose a novel semi-supervised, Multi-Level Sequential Generative Adversarial Network (MLS-GAN) architecture for group activity recognition. In contrast to previous works which utilise manually annotated individual human action predictions, we allow the models to learn it's own internal representations to discover pertinent sub-activities that aid the final group activity recognition task. The generator is fed with person-level and scene-level features that are mapped temporally through LSTM networks. Action-based feature fusion is performed through novel gated fusion units that are able to consider long-term dependencies, exploring the relationships among all individual actions, to learn an intermediate representation or action code' for the current group activity. The network achieves its semi-supervised behaviour by allowing it to perform group action classification together with the adversarial real fake validation. We perform extensive evaluations on different architectural variants to demonstrate the importance of the proposed architecture. Furthermore, we show that utilising both person-level and scene-level features facilitates the group activity prediction better than using only person-level features. Our proposed architecture outperforms current state-of-the-art results for sports and pedestrian based classification tasks on Volleyball and Collective Activity datasets, showing it's flexible nature for effective learning of group activities.
Some early works @cite_6 @cite_5 @cite_29 @cite_28 on group activity recognition have addressed the group activity recognition task on surveillance and sports video datasets with probabilistic and discriminative models that utilise hand-crafted features. As these hand-crafted feature based methods always require feature engineering, attention has shifted towards deep network based methods due to their automatic feature learning capability.
@cite_28 @cite_5 @cite_29 @cite_6
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We deal with the problem of recognizing social roles played by people in an event. Social roles are governed by human interactions, and form a fundamental component of human event description. We focus on a weakly supervised setting, where we are provided different videos belonging to an event class, without training role labels. Since social roles are described by the interaction between people in an event, we propose a Conditional Random Field to model the inter-role interactions, along with person specific social descriptors. We develop tractable variational inference to simultaneously infer model weights, as well as role assignment to all people in the videos. We also present a novel YouTube social roles dataset with ground truth role annotations, and introduce annotations on a subset of videos from the TRECVID-MED11 [1] event kits for evaluation purposes. The performance of the model is compared against different baseline methods on these datasets. In this paper we present a new framework for pedestrian action categorization. Our method enables the classification of actions whose semantic can be only analyzed by looking at the collective behavior of pedestrians in the scene. Examples of these actions are waiting by a street intersection versus standing in a queue. To that end, we exploit the spatial distribution of pedestrians in the scene as well as their pose and motion for achieving robust action classification. Our proposed solution employs extended Kalman filtering for tracking of detected pedestrians in 2D 1 2 scene coordinates as well as camera parameter and horizon estimation for tracker filtering and stabilization. We present a local spatio-temporal descriptor effective in capturing the spatial distribution of pedestrians over time as well as their pose. This descriptor captures pedestrian activity while requiring no high level scene understanding. Our work is tested against highly challenging real world pedestrian video sequences captured by low resolution hand held cameras. Experimental results on a 5-class action dataset indicate that our solution: i) is effective in classifying collective pedestrian activities; ii) is tolerant to challenging real world conditions such as variation in illumination, scale, viewpoint as well as partial occlusion and background motion; iii) outperforms state-of-the art action classification techniques. We present a hierarchical model for human activity recognition in entire multi-person scenes. Our model describes human behaviour at multiple levels of detail, ranging from low-level actions through to high-level events. We also include a model of social roles, the expected behaviours of certain people, or groups of people, in a scene. The hierarchical model includes these varied representations, and various forms of interactions between people present in a scene. The model is trained in a discriminative max-margin framework. Experimental results demonstrate that this model can improve performance at all considered levels of detail, on two challenging datasets.