Abstract: Detecting groups plays an important role for group activity detection. In this paper, we propose an automatic group activity detection by segmenting the video sequences automatically into dynamic clips. As the first step, groups are detected by adopting a bottom-up hierarchical clustering, where the number of groups is not provided beforehand. Then, groups are tracked over time to generate consistent trajectories. Furthermore, the Granger causality is used to compute the mutual effect between objects based on motion and appearances features. Finally, the Hierarchical Dirichlet Process is used to cluster the groups. Our approach not only detects the activity among the objects of a particular group (intra-group) but also extracts the activities among multiple groups (inter-group). The experiments on public datasets demonstrate the effectiveness of the proposed method. Although our approach is completely unsupervised, we achieved results with a clustering accuracy of up to 79.35 % and up to 81.94% on the Behave and the NUS-HGA datasets.

Abstract: In this paper, we propose a novel framework for unsupervised detection of object interactions in video sequences based on dynamic features. The goal of our system is to process videos in an unsupervised manner using Hierarchical Bayesian Topic Models, specifically the Hierarchical Dirichlet Processes (HDP). We investigate how low-level features such as optical flow combined with Hierarchical Dirichlet Process (HDP) can help to recognize meaningful interactions between objects in the scene, for example, in videos of animal interaction recordings, kicking ball, standing, moving around etc. The underlying hypothesis that we validate is that interactions in such scenarios are heavily characterized by their 2D spatio-temporal features. Various experiments have been performed on the challenging JAR-AIBO dataset and first promising results are reported.

Abstract: Extracting compound interactions involving multiple objects is a challenging task in computer vision due to different issues such as the mutual occlusions between objects, the varying group size and issues raised from the tracker. Additionally, the single activities are uncommon compared with the activities that are performed by two or more objects e.g. gathering, fighting, running, etc. The purpose of this paper is to address the problem of interaction recognition among multiple objects based on dynamic features in an unsupervised manner. Our main contribution is twofold. First, a combined framework using a tracking-by-detection framework for trajectory extraction and HDPs for latent interaction extraction is introduced. Another important contribution of this work is the introduction of a new dataset (the Cavy dataset). The Cavy dataset contains about six dominant interactions performed several times by two or three cavies at different locations. The cavies are interacting in complicated and unexpected ways, which leads to perform many interactions in a short time. This makes working on this dataset more challenging. The experiments in this study are not only performed on the Cavy dataset but to enrich the evaluation of our framework; we also use the benchmark dataset Behave. The experiments on these datasets demonstrate the effectiveness of the proposed method. Although the fact that our approach is completely unsupervised, we achieved satisfactory results with a clustering accuracy of up to 68.84% on the Behave dataset and up to 45% on Cavy dataset. Keywords: Interaction Detection, Multiple Object Tracking, Unsupervised Clustering, Hierarchical Dirichlet Processes.

Abstract: In this paper, we propose a novel framework for unsupervised detection of object interactions in video sequences based on dynamic features. The goal of our system is to process videos in an unsupervised manner using Hierarchical Bayesian Topic Models, specifically the Hierarchical Dirichlet Processes (HDP). We investigate how low-level features such as optical flow combined with Hierarchical Dirichlet Process (HDP) can help to recognize meaningful interactions between objects in the scene, for example, in videos of animal activity recordings, kicking ball, standing, moving around etc. The underlying hypothesis that we validate is that interaction in such scenarios are heavily characterized by their 2D spatio-temporal features. Various experiments have been performed on the challenging JAR-AIBO dataset and first promising results are reported.