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| 1 | A Novel Simple Visual Tracking Algorithm Based on Hashing and Deep Learning显示文摘Deep network has been proven efficient and robust to capture object features in some conditions.It still remains in the stage of classifying or detecting objects.In the field of visual tracking,deep network has not been applied widely.One of the reasons is that its time consuming made the strong method could not meet the speed need of visual tracking.A novel simple tracker is proposed to complete tracking task.A simple six-layer feed-forward backpropagation neural network is applied to capture object features.Nevertheless,this representation is not robust enough when illumination changes or drastic scale changes in dynamic condition.To improve the performance and not to increase much time spent,image perceptual hashing method is employed,which extracts low frequency information of object as its fingerprint to recognize the object from its structure.64-bit characters are calculated by it,and they are utilized to be the bias terms of the neutral network.This leads more significant improvement for performance of extracting sufficient object features.Then we take particle filter to complete the tracking process with the proposed representation.The experimental results demonstrate that the proposed algorithm is efficient and robust compared with the state-of-the-art tracking methods. | ZHU Suguo DU Junping REN Nan | 2017 | Chinese Journal of Electronics2017，26，5： | 14 |
| 2 | Robust visual tracking based on scale invariance and deep learning显示文摘 | Nan REN Junping DU Suguo ZHU Linghui LI Dan FAN JangMyung LEE | 2017 | Frontiers of Computer Science2017，11，2： | 2 |
| 3 | Hierarchical-Based Object Detection with Improved Locality Sparse Coding显示文摘This paper proposes to extend the hierarchical method to be adapted to sequential frames, aiming at detecting the moving object in dynamic scenes. A novel two-layer model is proposed, in which dictionaries are learned through three different stages and the locality constrained sparse representation is improved. This leads more significant improvement for performance of both static image classification and moving object detection. The experimental results demonstrate that the proposed algorithm is efficient and robust compared with the state-of-the-art classification methods, and also able to detect moving object in the sequential frames accurately. | ZHU Suguo DU Junping REN Nan LIANG Meiyu | 2016 | Chinese Journal of Electronics2016，25，2： | 2 |
| 4 | Mix Zone in Motion:Achieving Dynamically Cooperative Location Privacy Protection in Delay-tolerant Networks显示文摘 | Du Suguo Zhu Haojin Li Xiaolong | 2013 | IEEE Transactions on Vehicular Technology2013，62，9： | 1 |
| 5 | Fake Reviews Tell No Tales? Dissecting Click Farming in Content-Generated Social Networks显示文摘Recently, there has been a radial shift from traditional online social networks to content-generated social networks(CGSNs). Contemporary CGSNs, such as Dianping and Trip Advisor, are often the targets of click farming in which fake reviews are posted in order to boost or diminish the ratings of listed products and services simply through clicking. Click farming often emanates from a collection of multiple fake or compromised accounts, which we call click farmers. In this paper, we conduct a three-phase methodology to detect click farming. We begin by clustering communities based on newly-defined collusion networks. We then apply the Louvain community detection method to detecting communities. We finally perform a binary classification on detected-communities. Our results of over a year-long study show that(1) the prevalence of click farming is different across CGSNs;(2) most click farmers are lowly-rated;(3) click-farming communities have relatively tight relations between users;(4) more highly-ranked stores have a greater portion of fake reviews. | Neng Li Suguo Du Haizhong Zheng Minhui Xue Haojin Zhu | 2018 | China Communications2018，15，4： | 1 |
| 6 | UAV-assisted data gathering in wireless sensor networks显示文摘 | Mianxiong Dong Kaoru Ota Man Lin Zunyi Tang Suguo Du Haojin Zhu | 2014 | The Journal of Supercomputing2014，，3： | 1 |
| 7 | Adaptive multiple video sensors fusion based on decentralized Kalman filter and sensor confidence显示文摘The fusion of multiple video sensors provides an effective way to improve the robustness and accuracy of video surveillance systems. In this paper, an adaptive fusion method based on a decentralized Kalman filter(DKF) and sensor confidence is presented for the fusion of multiple video sensors. The adaptive scheme is one of the approaches used for preventing the divergence problem of the filter when statistical values of the measurement noises of the system models are not available. By introducing the sensor confidence, we can adaptively adjust the measurement noise covariance matrix of the local DKFs and thus, determine the weight of each sensor more correctly in the fusion procedure. Also, the DKF applied here can make full use of redundant tracking data from multiple video sensors and give more accurate fusion results in an efficient manner. Finally,the fusion result with improved accuracy is obtained. Experimental results show that the proposed adaptive decentralized Kalman filter fusion(ADKFF) method works well in the case of real-world video sequences and exhibits more promising performance than single sensors and comparative fusion methods. | Qingping LI Junping DU Suguo ZHU Liang XU | 2017 | Science China(Information Sciences)2017，60，6： | 0 |
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