Existing methods for multi-view gait-based identification mainly focus on transforming the features of one view to another view, which is technically sound but has limited practical utility. In this paper, we propose a view-invariant discriminative projection (ViDP) method, improve ability gait by unitary linear projection. It implemented iteratively learning low dimensional geometry and finding optimal according geometry. By virtue ViDP, can be directly matched without knowing or estimating...

Gait analysis provides a feasible approach for identification in intelligent video surveillance. However, the effectiveness of dominant silhouette-based approaches is overly dependent upon background subtraction. In this paper, we propose novel incremental framework based on optical flow, including dynamics learning, pattern retrieval, and recognition. It can greatly improve usability gait traits surveillance applications. Local binary (LBP) employed to describe texture information flow....

This paper proposes a supervised modeling approach for gait-based gender classification. Different from traditional temporal methods, male and female gait traits are competitively learned by the addition of labels. Shape appearance dynamics both genders integrated into sequential model called mixed conditional random field (CRF) (MCRF), which provides an open framework applicable to various spatiotemporal features. In this paper, spatial part, pyramids fitting coefficients used generate...

Heterogeneous graphs (HGs), consisting of multiple types nodes and links, can characterize a variety real-world complex systems. Recently, heterogeneous graph neural networks (HGNNs), as powerful embedding method to aggregate structure attribute information, has earned lot attention. Despite the ability HGNNs in capturing rich semantics which reveal different aspects nodes, they still stay at coarse-grained level simply exploits structural characteristics. In fact, unstructured text content...

In this paper, we address the problem of gait based gender classification. The Gabor feature which is a new attempt for analysis, not only improves robustness to segmental noise, but also provides feasible way purge additional influence factors like clothing and carrying condition changes before supervised learning. Furthermore, through agency Maximization Mutual Information (MMI), low dimensional discriminative representation obtained as Gabor-MMI feature. After that, related Gaussian...

Offshore oil and gas play a crucial role in meeting the energy demands of modern economies. However, risks associated with offshore activities, such as fires explosions, can lead to significant losses. Consequently, risk analysis operations is imperative. This paper introduces Bayesian network approach analyze activities. Initially, fuzzy set theory employed convert domain knowledge into quantitative prior probabilities. Insigh ts from industry experts literature then inform development...

In this paper, we propose a novel pattern to represent spatio-temporal information of gait appearance which is called Gait Principal Component Image (GPCI). GPCI grey-level image compresses the spatiotemporal by amplifying dynamic variation different body part. The detection period based on LLE coefficients and it also new attempt. KNN classifier employed for gender classification. framework can be applied in real-time setting because its rapidity robustness. experimental results IRIP...

With the rapid growth of interaction data, many clustering methods have been proposed to discover patterns as prior knowledge beneficial downstream tasks. Considering that an can be seen action occurring among multiple objects, most existing model objects and their pair-wise relations nodes links in graphs. However, they only leverage part information real entire interactions, i.e., either decompose into several sub-interactions for simplification, or focus on some specific types which...

Existing works mainly focus on crowd and ignore the confusion regions which contain extremely similar appearance to in background, while counting needs face these two sides at same time. To address this issue, we propose a novel end-to-end trainable region discriminating erasing network called CDENet. Specifically, CDENet is composed of modules mining module (CRM) guided (GEM). CRM consists basic density estimation (BDE) network, aware bridge network. The BDE first generates primary map,...

Scientific survey papers play a pivotal role in advancing knowledge and scientific progress by providing concise summaries analyses of research trends findings. To facilitate better organization analysis, we have undertaken the challenge defining entity recognition task for carefully curated dataset that closely emulates real-world scenarios. The presents unique challenges, including multi-label, low-resource, nested address these propose unified framework based on machine reading...

Besides identity, soft biometric characteristics, such as gender and age can also be derived from gait patterns. With Gabor enhancement, supervised learning temporal modelling, the authors present a robust framework to achieve state-of-the-art classification accuracy for both age. filter maximisation of mutual information are used extract low-dimensional features, whereas Bayes rules based on hidden Markov models (HMMs) adopted classification. The multi-view problem is defined two different...

View transformation in gait analysis has attracted more and attentions recently. However, most of the existing methods are based on entire dynamics, such as Gait Energy Image (GEI). And distinctive characteristics different walking phases neglected. This paper proposes a multi-view multi-stance identification method using unified population Hidden Markov Models (pHMM-s), which all models share same transition probabilities. Hence, dynamics each view can be normalized into fixed-length...

The recognition of ethnicity an individual can be very useful in a video-based surveillance system. In this paper, we propose multimodal biometric system involving integration frontal face and lateral gait, for the specific problem classification. This performs feature fusion to improve discrimination human ethnicity. Face features are extracted by means uniform LBP operator gait information is characterized spatio-temporal representation. Afterwards, canonical correlation analysis (CCA), as...

In a normal digital campus card system, three kinds of data have been collected, which are consuming records, entry and book borrowing records. Many researches used them to study capability distribution movement trajectory. this paper, we propose method describe social relationships predict frequencies based on students' behavior habits routines through data, can be support personalized education service university management. Inquiries show that the frequency values correctly estimate...

Prompt tuning attempts to update few task-specific parameters in pre-trained models. It has achieved comparable performance fine-tuning of the full parameter set on both language understanding and generation tasks. In this work, we study problem prompt for neural text retrievers. We introduce parameter-efficient retrieval across in-domain, cross-domain, cross-topic settings. Through an extensive analysis, show that strategy can mitigate two issues -- parameter-inefficiency weak...

Considering it is difficult to guarantee that at least one continuous complete gait cycle captured in real applications, we address the multi-view recognition problem with short probe sequences. With unified population hidden markov models (umvpHMMs), pattern represented as fixed-length stances. By incorporating multi-stance dynamics, well-known view transformation model (VTM) extended into a multi-linear projection four-order tensor space, so view-independent stance-independent identity...

Background subtraction is a key technique for video analysis applications. However, the existing algorithms do not work well in cluttered environments. In this work, we manage to model oscillating background by using multi-channel model, which constructed Gaussian filters with different variances. By employing boosting-like updating rule channel selection, evidence-driving Adaptive Modelling (ABM) framework proposed eliminate false foreground responses. The effectiveness of ABM tree and...