3,358 research outputs found
Unsupervised Learning of Video Representations via Dense Trajectory Clustering
This paper addresses the task of unsupervised learning of representations for
action recognition in videos. Previous works proposed to utilize future
prediction, or other domain-specific objectives to train a network, but
achieved only limited success. In contrast, in the relevant field of image
representation learning, simpler, discrimination-based methods have recently
bridged the gap to fully-supervised performance. We first propose to adapt two
top performing objectives in this class - instance recognition and local
aggregation, to the video domain. In particular, the latter approach iterates
between clustering the videos in the feature space of a network and updating it
to respect the cluster with a non-parametric classification loss. We observe
promising performance, but qualitative analysis shows that the learned
representations fail to capture motion patterns, grouping the videos based on
appearance. To mitigate this issue, we turn to the heuristic-based IDT
descriptors, that were manually designed to encode motion patterns in videos.
We form the clusters in the IDT space, using these descriptors as a an
unsupervised prior in the iterative local aggregation algorithm. Our
experiments demonstrates that this approach outperform prior work on UCF101 and
HMDB51 action recognition benchmarks. We also qualitatively analyze the learned
representations and show that they successfully capture video dynamics
Learning Deep Representations of Appearance and Motion for Anomalous Event Detection
We present a novel unsupervised deep learning framework for anomalous event
detection in complex video scenes. While most existing works merely use
hand-crafted appearance and motion features, we propose Appearance and Motion
DeepNet (AMDN) which utilizes deep neural networks to automatically learn
feature representations. To exploit the complementary information of both
appearance and motion patterns, we introduce a novel double fusion framework,
combining both the benefits of traditional early fusion and late fusion
strategies. Specifically, stacked denoising autoencoders are proposed to
separately learn both appearance and motion features as well as a joint
representation (early fusion). Based on the learned representations, multiple
one-class SVM models are used to predict the anomaly scores of each input,
which are then integrated with a late fusion strategy for final anomaly
detection. We evaluate the proposed method on two publicly available video
surveillance datasets, showing competitive performance with respect to state of
the art approaches.Comment: Oral paper in BMVC 201
Unsupervised Human Action Detection by Action Matching
We propose a new task of unsupervised action detection by action matching.
Given two long videos, the objective is to temporally detect all pairs of
matching video segments. A pair of video segments are matched if they share the
same human action. The task is category independent---it does not matter what
action is being performed---and no supervision is used to discover such video
segments. Unsupervised action detection by action matching allows us to align
videos in a meaningful manner. As such, it can be used to discover new action
categories or as an action proposal technique within, say, an action detection
pipeline. Moreover, it is a useful pre-processing step for generating video
highlights, e.g., from sports videos.
We present an effective and efficient method for unsupervised action
detection. We use an unsupervised temporal encoding method and exploit the
temporal consistency in human actions to obtain candidate action segments. We
evaluate our method on this challenging task using three activity recognition
benchmarks, namely, the MPII Cooking activities dataset, the THUMOS15 action
detection benchmark and a new dataset called the IKEA dataset. On the MPII
Cooking dataset we detect action segments with a precision of 21.6% and recall
of 11.7% over 946 long video pairs and over 5000 ground truth action segments.
Similarly, on THUMOS dataset we obtain 18.4% precision and 25.1% recall over
5094 ground truth action segment pairs.Comment: IEEE International Conference on Computer Vision and Pattern
Recognition CVPR 2017 Workshop
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
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