Real-time Human action classification in complex scenes has applications in various domains such as visual surveillance, video retrieval and human robot interaction. While, the task is challenging due to computation e...Real-time Human action classification in complex scenes has applications in various domains such as visual surveillance, video retrieval and human robot interaction. While, the task is challenging due to computation efficiency, cluttered backgrounds and intro-variability among same type of actions. Spatio-temporal interest point （STIP） based methods have shown promising results to tackle human action classification in complex scenes efficiently. However, the state-of-the-art works typically utilize bag-of-visual words （BoVW） model which only focuses on the word distribution of STIPs and ignore the distinctive character of word structure. In this paper, the distribution of STIPs is organized into a salient directed graph, which reflects salient motions and can be divided into a time salient directed graph and a space salient directed graph, aiming at adding spatio-temporal discriminant to BoVW. Generally speaking, both salient directed graphs are constructed by labeled STIPs in pairs. In detail, the ＂directional co-occurrence＂ property of different labeled pairwise STIPs in same frame is utilized to represent the time saliency, and the space saliency is reflected by the ＂geometric relationships＂ between same labeled pairwise STIPs across different frames. Then, new statistical features namely the Time Salient Pairwise feature （TSP） and the Space Salient Pairwise feature （SSP） are designed to describe two salient directed graphs, respectively. Experiments are carried out with a homogeneous kernel SVM classifier, on four challenging datasets KTH, ADL and UT-Interaction. Final results confirm the complementary of TSP and SSP, and our multi-cue representation TSP ＋ SSP ＋ BoVW can properly describe human actions with large intro-variability in real-time.展开更多
For vision-based mobile robot navigation, images of the same scene may undergo a general affine transformation in the case of significant viewpoint changes. So, a novel method for detecting affine invariant interest p...For vision-based mobile robot navigation, images of the same scene may undergo a general affine transformation in the case of significant viewpoint changes. So, a novel method for detecting affine invariant interest points is proposed to obtain the invariant local features, which is coined polynomial local orientation tensor(PLOT). The new detector is based on image local orientation tensor that is constructed from the polynomial expansion of image signal. Firstly, the properties of local orientation tensor of PLOT are analyzed, and a suitable tuning parameter of local orientation tensor is chosen so as to extract invariant features. The initial interest points are detected by local maxima search for the smaller eigenvalues of the orientation tensor. Then, an iterative procedure is used to allow the initial interest points to converge to affine invariant interest points and regions. The performances of this detector are evaluated on the repeatability criteria and recall versus 1-precision graphs, and then are compared with other existing approaches. Experimental results for PLOT show strong performance under affine transformation in the real-world conditions.展开更多
By mining of the requirements of lots of electric vehicle users for charging piles, this paper proposes the charging pile siting algorithm via the fusion of Points of Interest and vehicle trajectories. The proposed al...By mining of the requirements of lots of electric vehicle users for charging piles, this paper proposes the charging pile siting algorithm via the fusion of Points of Interest and vehicle trajectories. The proposed algorithm computes appropriate charging pile locations by: 1) mining user Points of Interest from social network; 2) mining parking sites of vehicle form GPS trajectories and 3) fusing the Points of Interest and parking sites together then clustering the fusions with our improved DBSCAN algorithm, whose clustering results indicates the final appropriate charging pile locations. Experimental results show that our proposed methods are more efficient than existing methods.展开更多
长春市是国家新型城镇化综合试点城市,识别长春市中心城区功能,针对当前存在的问题提出对策建议,对城市空间的优化与协调具有重要意义。以兴趣点(Point of Interest,POI)数据及开放街道地图(Open Street Map,OSM)数据为基础,结合核密度...长春市是国家新型城镇化综合试点城市,识别长春市中心城区功能,针对当前存在的问题提出对策建议,对城市空间的优化与协调具有重要意义。以兴趣点(Point of Interest,POI)数据及开放街道地图(Open Street Map,OSM)数据为基础,结合核密度分析、实地调查验证等方法,识别长春市中心城区城市功能类型。结果表明:单一功能区中,商业功能区数量最多,居住功能区最少;主导—混合功能区中的商业主导功能区及交通主导功能区形成对商业聚集区与轨道交通系统的重要补充;细分—混合功能区特征显示功能混合程度从市中心向周边逐渐加大。经验证,城市功能识别结果符合长春市实际,由此提出对策建议:未来长春市中心城区应注重多中心发展格局,并加强绿地空间和公共服务设施建设。展开更多
针对外卖配送电动自行车换电柜布局不合理带来的部分换电柜利用率低与部分换电需求得不到及时满足的供需矛盾问题,本文通过聚类POI(Point of Interest)数据确定外卖配送起止点,并通过仿真模拟外卖骑手配送路径预测外卖配送电动自行车换...针对外卖配送电动自行车换电柜布局不合理带来的部分换电柜利用率低与部分换电需求得不到及时满足的供需矛盾问题,本文通过聚类POI(Point of Interest)数据确定外卖配送起止点,并通过仿真模拟外卖骑手配送路径预测外卖配送电动自行车换电需求时空分布,构建换电柜运营商总成本最低和用户满意度最高的多目标换电柜选址定容模型,并以新乡市主城区为例,采用NSGA-II(Non-dominated Sorting Genetic Algorithm II)算法得到换电柜选址定容方案。研究结果表明:仿真模拟得出的换电需求时间分布预测值与实际值基本吻合,换电需求在11:00,14:00,17:00和20:00左右急剧增长,且11:00和14:00左右的换电需求量显著高于17:00和20:00左右的换电需求量,外卖骑手配送路径仿真模拟方法在换电需求预测上具有较高的预测精度;换电柜选址方案不能同时满足运营商和用户利益均为最优,用户满意度的提高需以增加运营商总成本为代价;同时,兼顾运营商和用户利益的新乡市主城区外卖配送电动自行车换电柜最佳建设数量为26,其中,容量为11的换电柜11个,容量为22的换电柜8个,容量为33的换电柜7个;新乡市主城区应按照备选点编号15-7-19-17依次新增换电柜至30个,此时,用户满意度最大,若继续增加换电柜建设数量,只会增加运营商总成本。展开更多
下一个兴趣点推荐是推荐算法领域的热点,旨在为用户推荐适合的下一地点。较新的研究通过图和序列方法模拟用户与POI的交互以及POI之间转换关系,性能得到显著提升。然而,现有模型仍然存在需要解决的问题。针对现有的下一个兴趣点推荐模...下一个兴趣点推荐是推荐算法领域的热点,旨在为用户推荐适合的下一地点。较新的研究通过图和序列方法模拟用户与POI的交互以及POI之间转换关系,性能得到显著提升。然而,现有模型仍然存在需要解决的问题。针对现有的下一个兴趣点推荐模型的局限性,特别是如何充分捕捉User-POI交互图上全局和局部信息,以及缓解图神经网络的过平滑特性导致图上信息丢失的问题,提出了基于graph Transformer的多编码模型(multi-coding network based on GT model)对下一个兴趣点进行推荐。首先,从位置和结构的视角上联合对user-POI交互图上进行全局、局部以及相对信息进行编码;然后,将编码后生成的图嵌入通过graph Transformer网络层更新图上节点与边信息;最后通过MLP网络层生成预测;最终,MCGT在Gowalla和TKY两个公开数据集进行对比实验。结果表明,在Gowalla数据集上recall和NDCG指标至少有3.79%的提升,在TKY数据集上recall和NDCG指标至少有2.5%的提升,证明了MCGT设计的合理性与有效性。展开更多
兴趣点(Point-Of-Interest,POI)推荐是基于位置的社交网络(Location-based Social Networks,LBSNs)研究中最重要的任务之一。为了解决POI推荐中的空间稀疏性问题,提出一种用于位置推荐的长短期偏好时空注意力网络(LSAN)。首先,构建了签...兴趣点(Point-Of-Interest,POI)推荐是基于位置的社交网络(Location-based Social Networks,LBSNs)研究中最重要的任务之一。为了解决POI推荐中的空间稀疏性问题,提出一种用于位置推荐的长短期偏好时空注意力网络(LSAN)。首先,构建了签到序列的时空关系矩阵,使用多头注意力机制从中提取非连续签到和非相邻位置中的时空相关性,缓解签到数据稀疏所带来的困难。其次,在模型中设置用户短期偏好和长期偏好提取模块,自适应的将二者结合在一起,考虑了用户偏好对用户决策影响。最后,在Foursquare数据集上进行测试,并与其它模型结果进行对比,证实了提出的LSAN模型结果最优。研究表明LSAN模型能够获得最佳的推荐效果,为POI推荐提供新思路。展开更多
现有的大多数兴趣点(point of interest,POI)推荐系统由于忽略了用户签到序列中的顺序行为模式,以及用户的个性化偏好对于POI推荐的影响,导致POI推荐系统性能较低,推荐结果不可靠,进而影响用户体验。为了解决上述问题,提出一种融合时序...现有的大多数兴趣点(point of interest,POI)推荐系统由于忽略了用户签到序列中的顺序行为模式,以及用户的个性化偏好对于POI推荐的影响,导致POI推荐系统性能较低,推荐结果不可靠,进而影响用户体验。为了解决上述问题,提出一种融合时序门控图神经网络的兴趣点推荐方法。运用时序门控图神经网络(temporal gated graph neural network,TGGNN)学习POI embedding;采用注意力机制捕获用户的长期偏好;通过注意力机制融合用户的最新偏好和实时偏好,进而捕获用户的短期偏好。通过自适应的方式结合用户的长期和短期偏好,计算候选POI的推荐得分,并根据得分为用户进行POI推荐。实验结果表明,与现有方法相比,该方法在召回率和平均倒数排名这两项指标上均有较为明显的提升,因此可以取得很好的推荐效果,具有良好的应用前景。展开更多
基金This work is supported by the National Natural Science Foundation of China （NSFC, nos. 61340046）, the National High Technology Research and Development Programme of China （863 Programme, no. 2006AA04Z247）, the Scientific and Technical Innovation Commission of Shenzhen Munici-pality （nos. JCYJ20130331144631730）, and the Specialized Research Fund for the Doctoral Programme of Higher Edu- cation （SRFDP, no. 20130001110011）.
文摘Real-time Human action classification in complex scenes has applications in various domains such as visual surveillance, video retrieval and human robot interaction. While, the task is challenging due to computation efficiency, cluttered backgrounds and intro-variability among same type of actions. Spatio-temporal interest point （STIP） based methods have shown promising results to tackle human action classification in complex scenes efficiently. However, the state-of-the-art works typically utilize bag-of-visual words （BoVW） model which only focuses on the word distribution of STIPs and ignore the distinctive character of word structure. In this paper, the distribution of STIPs is organized into a salient directed graph, which reflects salient motions and can be divided into a time salient directed graph and a space salient directed graph, aiming at adding spatio-temporal discriminant to BoVW. Generally speaking, both salient directed graphs are constructed by labeled STIPs in pairs. In detail, the ＂directional co-occurrence＂ property of different labeled pairwise STIPs in same frame is utilized to represent the time saliency, and the space saliency is reflected by the ＂geometric relationships＂ between same labeled pairwise STIPs across different frames. Then, new statistical features namely the Time Salient Pairwise feature （TSP） and the Space Salient Pairwise feature （SSP） are designed to describe two salient directed graphs, respectively. Experiments are carried out with a homogeneous kernel SVM classifier, on four challenging datasets KTH, ADL and UT-Interaction. Final results confirm the complementary of TSP and SSP, and our multi-cue representation TSP ＋ SSP ＋ BoVW can properly describe human actions with large intro-variability in real-time.
基金Projects(61203332,61203208) supported by the National Natural Science Foundation of China
文摘For vision-based mobile robot navigation, images of the same scene may undergo a general affine transformation in the case of significant viewpoint changes. So, a novel method for detecting affine invariant interest points is proposed to obtain the invariant local features, which is coined polynomial local orientation tensor(PLOT). The new detector is based on image local orientation tensor that is constructed from the polynomial expansion of image signal. Firstly, the properties of local orientation tensor of PLOT are analyzed, and a suitable tuning parameter of local orientation tensor is chosen so as to extract invariant features. The initial interest points are detected by local maxima search for the smaller eigenvalues of the orientation tensor. Then, an iterative procedure is used to allow the initial interest points to converge to affine invariant interest points and regions. The performances of this detector are evaluated on the repeatability criteria and recall versus 1-precision graphs, and then are compared with other existing approaches. Experimental results for PLOT show strong performance under affine transformation in the real-world conditions.
基金Supported by National Natural Science Foundation of China（61105089） State Key Laboratory of Robotics and System（SKLRS-2013-ZD-03） Open Foundation of the State Key Laboratory of Fluid Power Transmission and Control（GZKF-201212）
文摘By mining of the requirements of lots of electric vehicle users for charging piles, this paper proposes the charging pile siting algorithm via the fusion of Points of Interest and vehicle trajectories. The proposed algorithm computes appropriate charging pile locations by: 1) mining user Points of Interest from social network; 2) mining parking sites of vehicle form GPS trajectories and 3) fusing the Points of Interest and parking sites together then clustering the fusions with our improved DBSCAN algorithm, whose clustering results indicates the final appropriate charging pile locations. Experimental results show that our proposed methods are more efficient than existing methods.
文摘长春市是国家新型城镇化综合试点城市,识别长春市中心城区功能,针对当前存在的问题提出对策建议,对城市空间的优化与协调具有重要意义。以兴趣点(Point of Interest,POI)数据及开放街道地图(Open Street Map,OSM)数据为基础,结合核密度分析、实地调查验证等方法,识别长春市中心城区城市功能类型。结果表明:单一功能区中,商业功能区数量最多,居住功能区最少;主导—混合功能区中的商业主导功能区及交通主导功能区形成对商业聚集区与轨道交通系统的重要补充;细分—混合功能区特征显示功能混合程度从市中心向周边逐渐加大。经验证,城市功能识别结果符合长春市实际,由此提出对策建议:未来长春市中心城区应注重多中心发展格局,并加强绿地空间和公共服务设施建设。
文摘下一个兴趣点推荐是推荐算法领域的热点,旨在为用户推荐适合的下一地点。较新的研究通过图和序列方法模拟用户与POI的交互以及POI之间转换关系,性能得到显著提升。然而,现有模型仍然存在需要解决的问题。针对现有的下一个兴趣点推荐模型的局限性,特别是如何充分捕捉User-POI交互图上全局和局部信息,以及缓解图神经网络的过平滑特性导致图上信息丢失的问题,提出了基于graph Transformer的多编码模型(multi-coding network based on GT model)对下一个兴趣点进行推荐。首先,从位置和结构的视角上联合对user-POI交互图上进行全局、局部以及相对信息进行编码;然后,将编码后生成的图嵌入通过graph Transformer网络层更新图上节点与边信息;最后通过MLP网络层生成预测;最终,MCGT在Gowalla和TKY两个公开数据集进行对比实验。结果表明,在Gowalla数据集上recall和NDCG指标至少有3.79%的提升,在TKY数据集上recall和NDCG指标至少有2.5%的提升,证明了MCGT设计的合理性与有效性。
文摘兴趣点(Point-Of-Interest,POI)推荐是基于位置的社交网络(Location-based Social Networks,LBSNs)研究中最重要的任务之一。为了解决POI推荐中的空间稀疏性问题,提出一种用于位置推荐的长短期偏好时空注意力网络(LSAN)。首先,构建了签到序列的时空关系矩阵,使用多头注意力机制从中提取非连续签到和非相邻位置中的时空相关性,缓解签到数据稀疏所带来的困难。其次,在模型中设置用户短期偏好和长期偏好提取模块,自适应的将二者结合在一起,考虑了用户偏好对用户决策影响。最后,在Foursquare数据集上进行测试,并与其它模型结果进行对比,证实了提出的LSAN模型结果最优。研究表明LSAN模型能够获得最佳的推荐效果,为POI推荐提供新思路。
