Cuiling Lan

Online Human Action Detection Using Joint Classification-Regression Recurrent Neural Networks

Human action recognition from well-segmented 3D skeleton data has been intensively studied and has been attracting an increasing attention. Online action detection goes one step further and is more challenging, which identifies the action type and localizes the action positions on the fly from the untrimmed stream data. In this paper, we study the problem of online action detection from streaming skeleton data. We propose a multi-task end-to-end Joint Classification-Regression Recurrent Neural Network to better explore the action type and temporal localization information. By employing a joint classification and regression optimization objective, this network is capable of automatically localizing the start and end points of actions more accurately. Specifically, by leveraging the merits of the deep Long Short-Term Memory (LSTM) subnetwork, the proposed model automatically captures the complex long-range temporal dynamics, which naturally avoids the typical sliding window design and thus ensures high computational efficiency. Furthermore, the subtask of regression optimization provides the ability to forecast the action prior to its occurrence. To evaluate our proposed model, we build a large streaming video dataset with annotations. Experimental results on our dataset and the public G3D dataset both demonstrate very promising performance of our scheme.

Structure-Preserving Hybrid Digital-Analog Video Delivery in Wireless Networks

Hybrid digital-analog (HDA) transmission has gained increasing attention recently in the context of wireless video delivery , for its ability to simultaneously achieve high transmission efficiency and smooth quality adaptation. However, previous systems are optimized solely based on the mean squared error criterion without taking the perceptual video quality into consideration. In this work, we propose a structure-preserving HDA video delivery system, named SharpCast, to improve both the objective and subjective visual quality. SharpCast decomposes a video into a content part and structure part. The latter is important to the human perception and therefore is protected with a robust digital transmission scheme. Then, the energy-intensive part in the content information is extracted and transmitted in digital for energy efficiency while the residual is transmitted in analog to achieve the desired smooth adaptation. We formulate the resource (power and bandwidth) allocation problem in SharpCast and solve the problem with a greedy strategy. Evaluations over nine standard 720p video sequences show that the proposed SharpCast system outperforms the state-of-the-art digital, analog, and HDA schemes by a notable margin in both peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).

Progressive Pseudo-analog Transmission for Mobile Video Streaming

We propose a progressive pseudo-analog video transmission scheme that simultaneously handles SNR and bandwidth variations with graceful quality degradation for mobile video streaming. With the inherited SNR-adaptability from pseudo-analog transmission, the proposed progressive solution acquires bandwidth adaptability through an innovative scheduling algorithm with optimal power allocation. The basic idea is to aggressively transmit or retransmit important coefficients so that distortion is minimized at the receiver after each received packet. We derive the closed-form expression of reduced distortion for each packet under given transmission power and known channel conditions, and show that the optimal solution can be obtained with a water-filling algorithm. We also illustrate through analyses and simulations that a near-optimal solution can be found through approximation when only statistical channel information is available. Simulations show that our solution approaches the performance upper bound of pseudo-analog transmission in an additive white Gaussian noise channel and significantly outperforms existing pseudo-analog solutions in a fast Rayleigh fading channel. Trace-driven emulations are also carried out to demonstrate the advantage of the proposed solution over the state-of-the-art digital and pseudo-analog solutions under a real dramatically varying wireless environment.

Progressive pseudo-analog transmission for mobile video live streaming

Mobile video live streaming is facing great challenges in offering high quality of experience (QoE) under varying channel conditions. In this paper, we propose a progressive pseudo-analog transmission scheme in which the received video quality gracefully adapts to both SNR and bandwidth variations. Building upon the emerging pseudoanalog video transmission, the proposed scheme further adopts a greedy approach to improve the received video quality with each allocated bandwidth share. The optimal scheduling and power allocation are derived under the mean squared error (MSE) criterion. Testbed evaluations show that the proposed scheme outperforms the state-of-the-art digital and analog transmission schemes by a notable margin.

Adding Attentiveness to the Neurons in Recurrent Neural Networks

Recurrent neural networks (RNNs) are capable of modeling the temporal dynamics of complex sequential information. However, the structures of existing RNN neurons mainly focus on controlling the contributions of current and historical information but do not explore the different importance levels of different elements in an input vector of a time slot. We propose adding a simple yet effective Element-wise-Attention Gate (EleAttG) to an RNN block (e.g., all RNN neurons in a network layer) that empowers the RNN neurons to have the attentiveness capability. For an RNN block, an EleAttG is added to adaptively modulate the input by assigning different levels of importance, i.e., attention, to each element/dimension of the input. We refer to an RNN block equipped with an EleAttG as an EleAtt-RNN block. Specifically, the modulation of the input is content adaptive and is performed at fine granularity, being element-wise rather than input-wise. The proposed EleAttG, as an additional fundamental unit, is general and can be applied to any RNN structures, e.g., standard RNN, Long Short-Term Memory (LSTM), or Gated Recurrent Unit (GRU). We demonstrate the effectiveness of the proposed EleAtt-RNN by applying it to the action recognition tasks on both 3D human skeleton data and RGB videos. Experiments show that adding attentiveness through EleAttGs to RNN blocks significantly boosts the power of RNNs.

A Practical Hybrid Digital-Analog Scheme for Wireless Video Transmission

We propose a hybrid digital-analog framework for wireless video transmission, which benefits from both the high distortion-power performance of digital systems and the graceful performance degradation of analog systems. The proposed framework models video frames as a parallel Gaussian source, which is separated into digital and analog parts through scalar quantization. It features entropy coding and channel coding in digital transmission and power scaling in analog transmission. The key challenge in this framework is how to allocate the constrained power and bandwidth resources between and among digital and analog components to achieve minimal distortion at the receiver. Given the worst-case channel signal-to-noise ratio, we are able to derive a closed-form expression of the overall distortion. However, minimizing it is a mixed-integer non-linear programming problem, which is generally non-deterministic polynomial-time hard. By making reasonable and justified simplifications, we approach the optimal solution through a practical scheme. Evaluations show that the proposed scheme outperforms the state-of-the-art analog scheme SoftCast by a large margin. The gain in received video peak signal-to-noise ratio is up to 5.0 dB for various types of videos.

Internal-video mode dependent directional transform

As the projection of the real world, videos usually have many repeated patterns with similar structures cross regions, presenting strong non-local correlations. Moreover, different videos own different characteristics. Exploitation of the non-local correlations by off-line training of transforms has attracted considerable attention over the past years for compression. However, the samples used for training the transforms are usually collected from a predefined set of training videos to avoid the transmission of transform matrixes. There is no guarantee that the characteristics of those training videos and the corresponding transforms fit the current coding video very well. To address that, this paper proposes an internal-video mode dependent directional transform in HEVC for intra coding. In this scheme, for coding a video clip, based on different directions, a set of sample blocks is collected for each direction to train a Karhunen-Loeve transform at the video clip level. During encoding, the better transform between the proposed transform and original DCT/DST in HEVC is determined based on rate-distortion optimization. These transform matrixes are entropy coded to the bitstream. The proposed method can capture the statistical characteristics of the coded video clip and provide efficient transforms. Experimental results show that the proposed method achieves significant performance improvements in comparison with HEVC for intra coding, up to 13% BD-rate savings for the all intra configuration.

OMP-based transform for inter coding in HEVC

Discrete Cosine Transform (DCT) has been the commonly used transform for a few decades in image/video coding. However, DCT does not work well on the blocks having anisotropic correlations. In this paper, based on the adaptive dictionary, we propose a new online transform scheme using Orthogonal Matching Pursuit (OMP) for High Efficiency Video Coding (HEVC). For a coding block, we construct its dictionary by exploiting non-local correlations from the reconstructed regions. The OMP algorithm is implemented to obtain the sparse transform coefficients. Experimental results show that the BD-rate savings of the proposed scheme for the sequences with strong edges can be up to 19.9%.