Yonghong Hou

Generalised spatial modulation with multiple active transmit antennas

We propose a new generalised spatial modulation (GSM) technique, which can be considered as a generalisation of the recently proposed spatial modulation (SM) technique. SM can be seen as a special case of GSM with only one active transmit antenna. In contrast to SM, GSM uses the indices of multiple transmit antennas to map information bits, and is thus able to achieve substantially increased spectral efficiency. Furthermore, selecting multiple active transmit antennas enables GSM to harvest significant transmit diversity gains in comparison to SM, because all the active antennas transmit the same information. On the other hand, inter-channel interference (ICI) is completely avoided by transmitting the same symbols through these active antennas. We present theoretical analysis using order statistics for the symbol error rate (SER) performance of GSM. The analytical results are in close agreement with our simulation results. The bit error rate performance of GSM and SM is simulated and compared, which demonstrates the superiority of GSM. Moreover, GSM systems with configurations of different transmit and receive antennas are studied. Our results suggest that using a less number of transmit antennas with a higher modulation order will lead to better BER performance.

Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks

Recently, Convolutional Neural Networks (ConvNets) have shown promising performances in many computer vision tasks, especially image-based recognition. How to effectively use ConvNets for video-based recognition is still an open problem. In this paper, we propose a compact, effective yet simple method to encode spatio-temporal information carried in 3D skeleton sequences into multiple 2D images, referred to as Joint Trajectory Maps (JTM), and ConvNets are adopted to exploit the discriminative features for real-time human action recognition. The proposed method has been evaluated on three public benchmarks, i.e., MSRC-12 Kinect gesture dataset (MSRC-12), G3D dataset and UTD multimodal human action dataset (UTD-MHAD) and achieved the state-of-the-art results.

Skeleton Optical Spectra-Based Action Recognition Using Convolutional Neural Networks

This letter presents an effective method to encode the spatiotemporal information of a skeleton sequence into color texture images, referred to as skeleton optical spectra, and employs convolutional neural networks (ConvNets) to learn the discriminative features for action recognition. Such spectrum representation makes it possible to use a standard ConvNet architecture to learn suitable “dynamic” features from skeleton sequences without training millions of parameters afresh and it is especially valuable when there is insufficient annotated training video data. Specifically, the encoding consists of four steps: mapping of joint distribution, spectrum coding of joint trajectories, spectrum coding of body parts, and joint velocity weighted saturation and brightness. Experimental results on three widely used datasets have demonstrated the efficacy of the proposed method.

Joint Distance Maps Based Action Recognition With Convolutional Neural Networks

Motivated by the promising performance achieved by deep learning, an effective yet simple method is proposed to encode the spatio-temporal information of skeleton sequences into color texture images, referred to as joint distance maps (JDMs), and convolutional neural networks are employed to exploit the discriminative features from the JDMs for human action and interaction recognition. The pair-wise distances between joints over a sequence of single or multiple person skeletons are encoded into color variations to capture temporal information. The efficacy of the proposed method has been verified by the state-of-the-art results on the large RGB+D Dataset and small UTD-MHAD Dataset in both single-view and cross-view settings.

A novel rate control algorithm for video coding based on fuzzy-PID controller

Rate control algorithms (RCAs) aim to achieve the best visual quality under the minimum bit rate and the limited buffer size. A self-parameter-tuning fuzzy-PID controller is proposed to reduce the deviation between the target buffer level and the current buffer fullness. Fuzzy logic is used to tune each parameter of the proportional-integral-derivative controller by selecting appropriate fuzzy rules through simulation in H.264/advanced video coding (AVC). To control the quality fluctuation between consecutive frames, a quality controller is adopted. The proposed RCA has been implemented in an H.264/AVC video codec, and our experimental results show that the proposed algorithm achieves smooth target bits while enabling better buffer control and visual quality.

A Spectral and Spatial Approach of Coarse-to-Fine Blurred Image Region Detection

Blur exists in many digital images, it can be mainly categorized into two classes: defocus blur which is caused by optical imaging systems and motion blur which is caused by the relative motion between camera and scene objects. In this letter, we propose a simple yet effective automatic blurred image region detection method. Based on the observation that blur attenuates high-frequency components of an image, we present a blur metric based on the log averaged spectrum residual to get a coarse blur map. Then, a novel iterative updating mechanism is proposed to refine the blur map from coarse to fine by exploiting the intrinsic relevance of similar neighbor image regions. The proposed iterative updating mechanism can partially resolve the problem of differentiating an in-focus smooth region and a blurred smooth region. In addition, our iterative updating mechanism can be integrated into other image blurred region detection algorithms to refine the final results. Both quantitative and qualitative experimental results demonstrate that our proposed method is more reliable and efficient compared to various state-of-the-art methods.

Performance Optimization of Digital Spectrum Analyzer With Gaussian Input Signal

Analog to digital converters (ADC) and cascade integrator-comb (CIC) filters are the basic modules in a digital intermediate frequency (IF) spectrum analyzer. The optimal output signal-to-noise ratio (SNR) of the digital IF spectrum analyzer with the Gaussian input signal is considered in this letter. The idea is to strike a trade-off between the saturation error and granular error when quantizing the Gaussian input signal. This letter firstly derives a relationship among the maximum allowed input signal amplitude, input signal power, ADC quantization bits and optimal quantization SNR. Besides, an optimal clipping strategy for the CIC decimation filter with variable decimation rates is proposed. Both numerical and simulation results are presented to demonstrate that the proposed clipping method is able to achieve significant SNR gain compared with the traditional rounding or truncation method.

Combining ConvNets with hand-crafted features for action recognition based on an HMM-SVM classifier

This paper proposes a new framework for RGB-D-based action recognition that takes advantages of hand-designed features from skeleton data and deeply learned features from depth maps, and exploits effectively both the local and global temporal information. Specifically, depth and skeleton data are firstly augmented for deep learning and making the recognition insensitive to view variance. Secondly, depth sequences are segmented using the handcrafted features based on skeleton joints motion histogram to exploit the local temporal information. All training segments are clustered using an Infinite Gaussian Mixture Model (IGMM) through Bayesian estimation and labelled for training Convolutional Neural Networks (ConvNets) on the depth maps. Thus, a depth sequence can be reliably encoded into a sequence of segment labels. Finally, the sequence of labels is fed into a joint Hidden Markov Model and Support Vector Machine (HMM-SVM) classifier to explore the global temporal information for final recognition. The proposed framework was evaluated on the widely used MSRAction-Pairs, MSRDailyActivity3D and UTD-MHAD datasets and achieved promising results.

Skeleton-based action recognition using LSTM and CNN

Recent methods based on 3D skeleton data have achieved outstanding performance due to its conciseness, robustness, and view-independent representation. With the development of deep learning, Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM)-based learning methods have achieved promising performance for action recognition. However, for CNN-based methods, it is inevitable to loss temporal information when a sequence is encoded into images. In order to capture as much spatial-temporal information as possible, LSTM and CNN are adopted to conduct effective recognition with later score fusion. In addition, experimental results show that the score fusion between CNN and LSTM performs better than that between LSTM and LSTM for the same feature. Our method achieved state-of-the-art results on NTU RGB+D datasets for 3D human action analysis. The proposed method achieved 87.40% in terms of accuracy and ranked 1st place in Large Scale 3D Human Activity Analysis Challenge in Depth Videos.

Spatially and Temporally Structured Global to Local Aggregation of Dynamic Depth Information for Action Recognition

This paper presents an effective yet simple video representation for RGB-D-based action recognition. It proposes to represent a depth map sequence into three pairs of structured dynamic images (DIs) at body, part, and joint levels, respectively, through hierarchical bidirectional rank pooling. Different from previous works that applied one convolutional neural network (ConvNet) for each part/joint separately, one pair of structured DIs is constructed from depth maps at each granularity level and serves as the input of a ConvNet. The structured DI not only preserves the spatial-temporal information but also enhances the structure information across both body parts/joints and different temporal scales. In additionally, it requires low computational cost and memory to construct. This new representation, referred to as Spatially and Temporally Structured Dynamic Depth Images, aggregates from global to fine-grained levels motion and structure information in a depth sequence, and enables us to fine-tune the existing ConvNet models trained on image data for classification of depth sequences, without a need for training the models afresh. The proposed representation is evaluated on six benchmark data sets, namely, MSRAction3D, G3D, MSRDailyActivity3D, SYSU 3D HOI, UTD-MHAD, and M2I data sets and achieves the state-of-the-art results on all six data sets.