In this work we introduce a fully end-to-end approach for action detection in videos that learns to directly predict the temporal bounds of actions. Our intuition is that the process of detecting actions is naturally one of observation and refinement: observing moments in video, and refining hypotheses about when an action is occurring. Based on this insight, we formulate our model as a recurrent neural network-based agent that interacts with a video over time. The agent observes video frames and decides both where to look next and when to emit a prediction. Since backpropagation is not adequate in this non-differentiable setting, we use REINFORCE to learn the agent's decision policy. Our model achieves state-of-the-art results on the THUMOS'14 and ActivityNet datasets while observing only a fraction (2 or less) of the video frames.
Methods for spatio-temporal action detection have also been developed. Within the context of unconstrained'' internet videos, this includes a body of work on spatio-temporal action proposals @cite_47 @cite_20 @cite_40 @cite_51 @cite_0 @cite_10 @cite_28 . Analysis of broader surveillance scenes for action detection is also an active area of research. @cite_13 reason about groups of people, @cite_21 across multi-camera setups, and @cite_54 based on quadratic programming-based instantiations of rules. Common among these works is reasoning on spatio-temporal action proposals or human tracks, typically using sliding window-based approaches in the temporal dimension. Furthermore, these works are in the context of trimmed or constrained-setting video clips. In contrast, we address the task of temporal action detection in untrimmed, unconstrained videos, with an efficient method for determining which frames to examine.
@cite_13 @cite_28 @cite_10 @cite_54 @cite_21 @cite_0 @cite_40 @cite_47 @cite_51 @cite_20
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Spatio-temporal action detection in videos is typically addressed in a fully-supervised setup with manual annotation of training videos required at every frame. Since such annotation is extremely tedious and prohibits scalability, there is a clear need to minimize the amount of manual supervision. In this work we propose a unifying framework that can handle and combine varying types of less-demanding weak supervision. Our model is based on discriminative clustering and integrates different types of supervision as constraints on the optimization. We investigate applications of such a model to training setups with alternative supervisory signals ranging from video-level class labels over temporal points or sparse action bounding boxes to the full per-frame annotation of action bounding boxes. Experiments on the challenging UCF101-24 and DALY datasets demonstrate competitive performance of our method at a fraction of supervision used by previous methods. The flexibility of our model enables joint learning from data with different levels of annotation. Experimental results demonstrate a significant gain by adding a few fully supervised examples to otherwise weakly labeled videos. Spatio-temporal detection of actions and events in video is a challenging problem. Besides the difficulties related to recognition, a major challenge for detection in video is the size of the search space defined by spatio-temporal tubes formed by sequences of bounding boxes along the frames. Recently methods that generate unsupervised detection proposals have proven to be very effective for object detection in still images. These methods open the possibility to use strong but computationally expensive features since only a relatively small number of detection hypotheses need to be assessed. In this paper we make two contributions towards exploiting detection proposals for spatio-temporal detection problems. First, we extend a recent 2D object proposal method, to produce spatio-temporal proposals by a randomized supervoxel merging process. We introduce spatial, temporal, and spatio-temporal pairwise supervoxel features that are used to guide the merging process. Second, we propose a new efficient supervoxel method. We experimentally evaluate our detection proposals, in combination with our new supervoxel method as well as existing ones. This evaluation shows that our supervoxels lead to more accurate proposals when compared to using existing state-of-the-art supervoxel methods. We introduce an approach for spatio-temporal human action localization using sparse spatial supervision. Our method leverages the large amount of annotated humans available today and extracts human tubes by combining a state-of-the-art human detector with a tracking-by-detection approach. Given these high-quality human tubes and temporal supervision, we select positive and negative tubes with very sparse spatial supervision, i.e., only one spatially annotated frame per instance. The selected tubes allow us to effectively learn a spatio-temporal action detector based on dense trajectories or CNNs. We conduct experiments on existing action localization benchmarks: UCF-Sports, J-HMDB and UCF-101. Our results show that our approach, despite using sparse spatial supervision, performs on par with methods using full supervision, i.e., one bounding box annotation per frame. To further validate our method, we introduce DALY (Daily Action Localization in YouTube), a dataset for realistic action localization in space and time. It contains high quality temporal and spatial annotations for 3.6k instances of 10 actions in 31 hours of videos (3.3M frames). It is an order of magnitude larger than existing datasets, with more diversity in appearance and long untrimmed videos. This paper tackles the problem of spatio-temporal action localization in a video, without assuming the availability of multiple videos or any prior annotations. Action is localized by employing images downloaded from internet using action name. Given web images, we first dampen image noise using random walk and evade distracting backgrounds within images using image action proposals. Then, given a video, we generate multiple spatio-temporal action proposals. We suppress camera and background generated proposals by exploiting optical flow gradients within proposals. To obtain the most action representative proposals, we propose to reconstruct action proposals in the video by leveraging the action proposals in images. Moreover, we preserve the temporal smoothness of the video and reconstruct all proposal bounding boxes jointly using the constraints that push the coefficients for each bounding box toward a common consensus, thus enforcing the coefficient similarity across multiple frames. We solve this optimization problem using variant of two-metric projection algorithm. Finally, the video proposal that has the lowest reconstruction cost and is motion salient is used to localize the action. Our method is not only applicable to the trimmed videos, but it can also be used for action localization in untrimmed videos, which is a very challenging problem. We present extensive experiments on trimmed as well as untrimmed datasets to validate the effectiveness of the proposed approach.