For compressed sensing, to address problems which do not involve reconstruction, a correlation analysis between measurements and the transform coefficients is proposed. It is shown that there is a linear relationship between them, which indicates that we can abstract the inner property of images directly in the measurement domain.

This work presents a real-time human detection system for VGA (Video Graphics Array, 640480) video, which well suits visual surveillance applications. To achieve high running speed and accuracy, firstly we design multiple fast scalar feature types on the gradient channels, and experimentally identify that NOGCF (Normalized Oriented Gradient Channel Feature) has better performance with Gentle AdaBoost in cascaded classifiers. A confidence measure for cascaded classifiers is developed and utilized in the subsequent tracking stage. Secondly, we propose to use speedup techniques including a detector pyramid for multi-scale detection and channel compression for integral channel calculation respectively. Thirdly, by integrating the detector's discrete detected humans and continuous detection confidence map, we employ a two-layer tracking by detection algorithm for further speedup and accuracy improvement. Compared with other methods, experiments show the system is significantly faster with 20 fps running speed in VGA video and has better accuracy as well.

This paper proposes a high-accuracy sub-pixel registration framework based on phase correlation for noisy images. First we introduce a denoising module, where the edge-preserving filter is adopted. This strategy not only filters off the noise but also preserves most of the original image signal. A confidence-weighted optimization module is then proposed to fit the linear phase plane discriminately and to achieve sub-pixel shifts. Experiments demonstrate the effectiveness of the combination of our modules and improvements of the accuracy and robustness against noise compared to other sub-pixel phase correlation methods in the Fourier domain.

Distributed compressed video sensing (DCVS), combining advantages of compressed sensing and distributed video coding, is developed as a novel and powerful system to get an encoder with low complexity. Nevertheless, it is still unclear how to explore the method to achieve an effective video recovery through utilizing realistic signal characteristics as much as possible. Based on this, we present a novel spatiotemporal dictionary learning (DL) based reconstruction method for DCVS, where both the DL model and the l1-analysis based recovery with correlation constraints are included in the minimization problem to achieve the joint optimization of sparse representation and signal reconstruction. Besides, an alternating direction method with multipliers (ADMM) based numerical algorithm is outlined for solving the underlying optimization problem. Simulation results demonstrate that the proposed method outperforms other methods, with 0.03-4.14 dB increases in PSNR and a 0.13-15.31 dB gain for non-key frames.

A direct inter-mode selection algorithm for P-frames in fast homogeneous H.264/AVC bit-rate reduction transcoding is proposed in this paper. To achieve the direct inter-mode selection, we firstly develop a low-complexity distortion estimation method for fast transcoding, in which the distortion is directly calculated from the decoded residual together with the reference frames. We also present a linear estimation method to approximate the coding rate. With the estimated distortion and rate, the rate-distortion cost can be easily computed in the transcoder. In our algorithm, a method based on the normalized rate difference of P-frames (RP) is used to detect the high motion scene. To achieve fast transcoding, only for the P-frames with RP larger than a threshold, the rate-distortion optimized (RDO) mode decision is performed; meanwhile, the average cost of each inter-mode (ACM) is calculated. Then for the subsequent frames transcoding, the optimal coding mode can be directly selected using the estimated cost and the ACM threshold. Experiments show that the proposed method can significantly simplify the complex RDO mode decision, and achieve transcoding time reductions of up to 62% with small loss of rate-distortion performance.

With the development of Global Navigation Satellite System (GNSS), the amount of related research is growing rapidly in China. A lot of accomplishments have been achieved in all branches of the satellite navigation field, especially motivated by the BeiDou Program. In this paper, the current status, technologies and developments in satellite positioning and navigation in China are introduced. Firstly, an overview and update of the BeiDou Program is presented, known as the three-step development strategy for different services. Then signal design for the BeiDou system is discussed, including the generation of pseudo-random noise (PRN) codes for currently available signal B1, and the investigation of a new signal modulation scheme for interoperability at open frequency B1C. The B1C signal should comply to Multiplexed Binary Offset Carrier (MBOC) constrains, and a modulation called Quadrature Multiplexed BOC (QMBOC) is presented, which is equivalent to time-multiplexed BOC (TMBOC) for GPS and composite BOC (CBOC) for Galileo, while overcomes the drawback of CBOC. Besides, the inter and intra system compatibility is discussed, based on the effective C/N0 proposed by International Telecommunication Union (ITU). After that, receiver technologies in challenging environments are introduced, such as weak signal acquisition and assisted GNSS (A-GNSS). Moreover, a method of ambiguity mitigation for adaptive digital beam forming (ADBF) in large spacing antenna arrays is proposed, by which interference suppression is available. Furthermore, cutting edge technologies are brought in, including seamless navigation for indoor and outdoor, and collaborative navigation. After all, GNSS applications in China for industry and daily life are shown, as well as the market prospection.

Boosting over weak classifiers is widely used in pedestrian detection. As the number of weak classifiers is large, researchers always use a sampling method over weak classifiers before training. The sampling makes the boosting process harder to reach the fixed target. In this paper, we propose a partial derivative guidance for weak classifier mining method which can be used in conjunction with a boosting algorithm. Using weak classifier mining method makes the sampling less degraded in the performance. It has the same effect as testing more weak classifiers while using acceptable time. Experiments demonstrate that our algorithm can process quicker than [1] algorithm in both training and testing, without any performance decrease. The proposed algorithms is easily extending to any other boosting algorithms using a window-scanning style and HOG-like features.

Person re-identification is challenging due to illumination changes and viewpoint variations in the multi-camera environment. In this paper, we propose a novel spatial pyramid color representation (SPCR) and a local region matching scheme, to explore person appearance for re-identification. SPCR effectively integrates color layout into histogram, forming an informative global feature. Local region matching utilizes region statistics, which is described by covariance feature, to find appearance correspondence locally. Our approach shows robustness to illumination changes and slight viewpoint variations. Experiments on a public dataset demonstrate the performance superiority of our proposal over state-of-the-art methods.

In this paper, a multi-data and multi-ACK verified selective forwarding attacks (SFAs) detection scheme is proposed for containing SFAs. In our scheme, each node (in addition to the nodes in the hotspots area) generates multiple acknowledgement (ACK) message for each received packet to confirm the normal packet transmission. In multiple ACK message, one ACK is returned along the data forwarding path, other ACKs are returned along different routing paths, and thus malicious nodes can be located accurately. At the same time, source node send multiple data routing, one is primary data routing, the others are backup data routing. Primary data is routed to sink directly, but backup data is routed to nodes far from sink, and then waits for the returned ACK of sink when primary data is routed to sink. If a node doesn't receive the ACK, the backup data is routed to sink, thus the success rate of data transmission and lifetime can be improved. For this case, the MDMA scheme has better potential to detect abnormal packet loss and identify suspect nodes as well as resilience against attack. Theoretical analysis and experiments show that MDMA scheme has better ability for ensuring success rate of data transmission, detecting SFA and identifying malicious nodes.

Learning an appearance model for person re-identification from multiple images is challenging due to the corrupted images caused by occlusion or false detection. Furthermore, different persons may wear similar clothes, making appearance feature less discriminative. In this paper, we first introduce the concept of multiple instance to handle corrupted images. Then a novel pairwise comparison based multiple instance learning framework is proposed to deal with visual ambiguity, by selecting robust features through pairwise comparison. We demonstrate the effectiveness of our method on two public datasets.

To improve the recognition rate of the end-to-end modulation recognition method based on deep learning, a modulation recognition method of communication signals based on a cascade network is proposed, which is composed of two networks: Stacked Denoising Auto Encoder (SDAE) network and DCELDNN (Dilated Convolution, ECA Mechanism, Long Short-Term Memory, Deep Neural Networks) network. SDAE network is used to denoise the data, reconstruct the input data through encoding and decoding, and extract deep information from the data. DCELDNN network is constructed based on the CLDNN (Convolutional, Long Short-Term Memory, Fully Connected Deep Neural Networks) network. In the DCELDNN network, dilated convolution is used instead of normal convolution to enlarge the receptive field and extract signal features, the Efficient Channel Attention (ECA) mechanism is introduced to enhance the expression ability of the features, the feature vector information is integrated by a Global Average Pooling (GAP) layer, and signal features are extracted by the DCELDNN network efficiently. Finally, end-to-end classification recognition of communication signals is realized. The test results on the RadioML2018.01a dataset show that the average recognition accuracy of the proposed method reaches 63.1% at SNR of -10 to 15 dB, compared with CNN, LSTM, and CLDNN models, the recognition accuracy is improved by 25.8%, 12.3%, and 4.8% respectively at 10 dB SNR.