This paper addresses the problem of real-time action recognition in trimmed videos, for which deep neural networks have defined the state-of-the-art performance in the recent literature. For attaining higher recognition accuracies with efficient computations, researchers have addressed the various aspects of limitations in the recognition pipeline. This includes network architecture, the number of input streams (where additional streams augment the color information), the cost function to be optimized, in addition to others. The literature has always aimed, though, at assigning the adopted network (or networks, in case of multiple streams) the task of recognizing the whole number of action classes in the dataset at hand. We propose to train multiple specialized module networks instead. Each module is trained to recognize a subset of the action classes. Towards this goal, we present a dissimilarity-based optimized procedure for distributing the action classes over the modules, which can be trained simultaneously offline. On two standard datasets--UCF-101 and HMDB-51--the proposed method demonstrates a comparable performance, that is superior in some aspects, to the state-of-the-art, and that satisfies the real-time constraint. We achieved 72.5 accuracy on the challenging HMDB-51 dataset. By assigning fewer and unalike classes to each module network, this research paves the way to benefit from light-weight architectures without compromising recognition accuracy.
Recently, different strategies employing 3D CNNs have outperformed the existing 2D CNNs approaches, such as C3D @cite_10 and 3D ResNets @cite_2 . 2D CNNs and their variants that are pre-trained on ImageNet were very successful in image recognition tasks. 3D CNNs perform much better with regards to action recognition as well @cite_0 . Lately, Kinetics--a large-scale dataset comparable to ImageNet in terms of volume--offered a remarkable improvement in recognition accuracies when used in pre-training deep networks @cite_2 . Hara et. al, explored ResNet models with a different number of layers and architectures, pre-trained on Kinetics. They achieved a top accuracy of 90.7 To the best of our knowledge, only a limited number of techniques are available in the literature of real-time deep action recognition. One example is the method proposed by Zhang et. al. @cite_5 . They employed a two-stream CNN architecture with multiple transfer learning techniques in order to make effective use of motion vectors, instead of optical flow. Hence, this approach improves the overall system performance by speeding up inference for a reasonable degradation in accuracy level. The main focus of this paper is speeding up the processing time without compromising the performance.
@cite_0 @cite_5 @cite_10 @cite_2
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The paucity of videos in current action classification datasets (UCF-101 and HMDB-51) has made it difficult to identify good video architectures, as most methods obtain similar performance on existing small-scale benchmarks. This paper re-evaluates state-of-the-art architectures in light of the new Kinetics Human Action Video dataset. Kinetics has two orders of magnitude more data, with 400 human action classes and over 400 clips per class, and is collected from realistic, challenging YouTube videos. We provide an analysis on how current architectures fare on the task of action classification on this dataset and how much performance improves on the smaller benchmark datasets after pre-training on Kinetics. We also introduce a new Two-Stream Inflated 3D ConvNet (I3D) that is based on 2D ConvNet inflation: filters and pooling kernels of very deep image classification ConvNets are expanded into 3D, making it possible to learn seamless spatio-temporal feature extractors from video while leveraging successful ImageNet architecture designs and even their parameters. We show that, after pre-training on Kinetics, I3D models considerably improve upon the state-of-the-art in action classification, reaching 80.9 on HMDB-51 and 98.0 on UCF-101. The two-stream CNNs prove very successful for video-based action recognition. However, the classical two-stream CNNs are time costly, mainly due to the bottleneck of calculating optical flows (OFs). In this paper, we propose a two-stream-based real-time action recognition approach by using motion vector (MV) to replace OF. MVs are encoded in video stream and can be extracted directly without extra calculation. However, directly training CNN with MVs degrades accuracy severely due to the noise and the lack of fine details in MVs. In order to relieve this problem, we propose four training strategies which leverage the knowledge learned from OF CNN to enhance the accuracy of MV CNN. Our insight is that MV and OF share inherent similar structures which allow us to transfer knowledge from one domain to another. To fully utilize the knowledge learned in OF domain, we develop deeply transferred MV CNN. Experimental results on various datasets show the effectiveness of our training strategies. Our approach is significantly faster than OF based approaches and achieves processing speed of 390.7 frames per second, surpassing real-time requirement. We release our model and code to facilitate further research. 1 1 https: github.com zbwglory MV-release The purpose of this study is to determine whether current video datasets have sufficient data for training very deep convolutional neural networks (CNNs) with spatio-temporal three-dimensional (3D) kernels. Recently, the performance levels of 3D CNNs in the field of action recognition have improved significantly. However, to date, conventional research has only explored relatively shallow3Darchitectures. We examine the architectures of various 3D CNNs from relatively shallow to very deep ones on current video datasets. Based on the results of those experiments, the following conclusions could be obtained: (i) ResNet-18 training resulted in significant overfitting for UCF-101, HMDB-51, and ActivityNet but not for Kinetics. (ii) The Kinetics dataset has sufficient data for training of deep 3D CNNs, and enables training of up to 152 ResNets layers, interestingly similar to 2D ResNets on ImageNet. (iii) Kinetics pretrained simple 3D architectures outperforms complex2D architectures, and the pretrained ResNeXt-101 achieved 94.5 and 70.2 on UCF-101 and HMDB-51, respectively. The use of 2D CNNs trained on ImageNet has produced significant progress in various tasks in image. We believe that using deep 3D CNNs together with Kinetics will retrace the successful history of 2D CNNs and ImageNet, and stimulate advances in computer vision for videos. The codes and pretrained models used in this study are publicly available.