Existing anomaly detection (AD) approaches rely on various hand‐crafted representations to represent video data and can be costly. The choice or designing of hand‐crafted representation can be difficult when faced with a new dataset without prior knowledge. Motivated by feature learning, e.g. deep leaning and the ability to directly learn useful representations and model high‐level abstraction from raw data, the authors investigate the possibility of using a universal approach. The objective is learning data‐driven high‐level representation for the task of video AD without relying on hand‐crafted representation. A deep incremental slow feature analysis (D‐IncSFA) network is constructed and applied to directly learning progressively abstract and global high‐level representations from raw data sequence. The D‐IncSFA network has the functionalities of both feature extractor and anomaly detector that make AD completion in one step. The proposed approach can precisely detect global anomaly such as crowd panic. To detect local anomaly, a set of anomaly maps, produced from the network at different scales, is used. The proposed approach is universal and convenient, working well in different types of scenarios with little human intervention and low memory and computational requirements. The advantages are validated by conducting extensive experiments on different challenge datasets.

Domain adaptation (DA) aims to generalize a learning model across training and testing data despite the mismatch of their data distributions. In light of a theoretical estimation of the upper error bound, we argue, in this article, that an effective DA method for classification should: 1) search a shared feature subspace where the source and target data are not only aligned in terms of distributions as most state-of-the-art DA methods do but also discriminative in that instances of different classes are well separated and 2) account for the geometric structure of the underlying data manifold when inferring data labels on the target domain. In comparison with a baseline DA method which only cares about data distribution alignment between source and target, we derive three different DA models for classification, namely, close yet discriminative DA (CDDA), geometry-aware DA (GA-DA), and discriminative and GA-DA (DGA-DA), to highlight the contribution of CDDA based on 1), GA-DA based on 2), and, finally, DGA-DA implementing jointly 1) and 2). Using both the synthetic and real data, we show the effectiveness of the proposed approach which consistently outperforms the state-of-the-art DA methods over 49 image classification DA tasks through eight popular benchmarks. We further carry out an in-depth analysis of the proposed DA method in quantifying the contribution of each term of our DA model and provide insights into the proposed DA methods in visualizing both real and synthetic data.

This paper presents a novel scheme for image-based visual servoing (IBVS) of a robot manipulator by considering robot dynamics without using joint velocity measurements in the presence of constraints, uncalibrated camera intrinsic and extrinsic parameters and unknown feature position parameters. An approach to design model predictive control (MPC) method based on identification algorithm and sliding mode observer has been proposed. Based on the MPC method, the IBVS tasks can be considered as a nonlinear optimization problem, while the constraints due to the visibility constraint and the torque constraint can be explicitly taken into account. By using the depth-independent interaction matrix framework, the identification algorithm can be used to update the unknown parameters and the prediction model. In addition, many existing controllers require the joint velocity measurements, which can be contaminated by noises, thus resulting in the IBVS performance degradation. To overcome the problem without joint velocity measurements, the sliding mode observer is designed to estimate the joint velocities of the IBVS system. Simulation results for both eye-in-hand and eye-to-hand camera configurations are presented to verify the effectiveness of the proposed control method.