multiple people detection and tracking is a challenging task in real-world crowded scenes. in this paper, we have presented an online multiple people tracking-by-detection approach with a single camera. we have detected objects with deformable part models and a visual background extractor. in the tracking phase we have used a combination of support vector machine (svm) person-specific classifie...

Multiple people detection and tracking is a challenging task in real-world crowded scenes. In this paper, we have presented an online multiple people tracking-by-detection approach with a single camera. We have detected objects with deformable part models and a visual background extractor. In the tracking phase we have used a combination of support vector machine (SVM) person-specific classifie...

Online learning has turned out to be effective for improving tracking performance. However, it could simply applied classification branch, but still remains challenging adapt regression branch due its complex design and intrinsic requirement high-quality online samples. To tackle this issue, we present the fully convolutional framework, coined as FCOT, focus on enabling both branches by using a...

With the launch of ultra-portable systems, mobile eye tracking finally has the potential to become mainstream. While eye movements on their own can already be used to identify human activities, such as reading or walking, linking eye movements to objects in the environment provides even deeper insights into human cognitive processing. We present a model-based approach for the identification of ...

In this paper, an adaptive fuzzy tracking control approach is proposed for a class of single-inputsingle-output (SISO) nonlinear systems in which the unknown continuous functions may be nonlinearlyparameterized. During the controller design procedure, the fuzzy logic systems (FLS) in Mamdani type are applied to approximate the unknown continuous functions, and then, based on the minimal learnin...