A Bionic Spatial Cognition Model and Method for Robots Based on the Hippocampus Mechanism

The hippocampus and its accessory are the main areas for spatial cognition. It can integrate paths and form environmental cognition based on motion information and then realize positioning and navigation. Learning from the hippocampus mechanism is a crucial way forward for research in robot perception, so it is crucial to building a calculation method that conforms to the biological principle. In addition, it should be easy to implement on a robot. This paper proposes a bionic cognition model and method for mobile robots, which can realize precise path integration and cognition of space. Our research can provide the basis for the cognition of the environment and autonomous navigation for bionic robots.

Real-Time Analysis of Athletes’ Physical Condition in Training Based on Video Monitoring Technology of Optical Flow Equation

This article is dedicated to the research of video motion segmentation algorithms based on optical flow equations. First, some mainstream segmentation algorithms are studied, and on this basis, a segmentation algorithm for spectral clustering analysis of athletes’ physical condition in training is proposed. After that, through the analysis of the existing methods, compared with some algorithms that only process a single frame in the video, this article analyzes the continuous multiple frames in the video and extracts the continuous multiple frames of the sampling points through the Lucas-Kanade optical flow method. We densely sampled feature points contain as much motion information as possible in the video and then express this motion information through trajectory description and finally achieve segmentation of moving targets through clustering of motion trajectories. At the same time, the basic concepts of image segmentation and video motion target segmentation are described, and the division standards of different video motion segmentation algorithms and their respective advantages and disadvantages are analyzed. The experiment determines the initial template by comparing the gray-scale variance of the image, uses the characteristic optical flow to estimate the search area of the initial template in the next frame, reduces the matching time, judges the template similarity according to the Hausdorff distance, and uses the adaptive weighted template update method for the templates with large deviations. The simulation results show that the algorithm can achieve long-term stable tracking of moving targets in the mine, and it can also achieve continuous tracking of partially occluded moving targets.

Parabolic Detection Algorithm of Tennis Serve Based on Video Image Analysis Technology

At present, the existing algorithm for detecting the parabola of tennis serves neglects the pre-estimation of the global motion information of tennis balls, which leads to great error and low recognition rate. Therefore, a new algorithm for detecting the parabola of tennis service based on video image analysis is proposed. The global motion information is estimated in advance, and the motion feature of the target is extracted. A tennis appearance model is established by sparse representation, and the data of high-resolution tennis flight appearance model are processed by data fusion technology to track the parabolic trajectory. Based on the analysis of the characteristics of the serve mechanics, according to the nonlinear transformation of the parabolic trajectory state vector, the parabolic trajectory starting point is determined, the parabolic trajectory is obtained, and the detection algorithm of the parabolic service is designed. Experimental results show that compared with the other two algorithms, the algorithm designed in this paper can recognize the trajectory of the parabola at different stages, and the detection accuracy of the parabola is higher in the three-dimensional space of the tennis service.

The Contribution of Visual Area V5 to the Perception of Implied Motion in Art and Its Appreciation

Does V5, a brain region involved in the perception of movement, contribute to the aesthetic appreciation of artworks that depict movement? In the study under discussion, the authors asked participants to view abstract and representational artworks depicting motion. While they judged the sense of motion conveyed by the artworks and how much they liked them, the authors delivered transcranial magnetic stimulation (TMS) over V5. They found that TMS over V5 reduced the sense of motion participants perceived and reduced how much participants liked the abstract paintings. These results show, first, that V5 is involved in extracting implied motion information even when the object whose motion is implied is not real. Second, they show that V5 is involved in extracting implied motion information even in the absence of any object, as in the abstract paintings. Finally, they show that activity in V5 plays a causal role in the appreciation of abstract art.

Application of flexible friction nano-generator in human motion information acquisition

Under the background of the wide application of intelligent wearable devices, the application of flexible friction nanogenerator in human motion information acquisition is studied. According to the actual needs of energy supply of wearable electronic devices and human motion information acquisition, a flexible friction nanogenerator was prepared by using polyester fiber nickel plated conductive cloth and room temperature vulcanized silica gel polymer as friction positive and negative materials for human motion information acquisition. Set relevant parameters for test. The output peaks of short-circuit current and open circuit voltage are 5 respectively μA and 50 V. The test shows that the output energy can drive the calculator and digital clock to work in real time, and can realize the collection of human motion information.

Integrated Multiscale Appearance Features and Motion Information Prediction Network for Anomaly Detection

The rise of video-prediction algorithms has largely promoted the development of anomaly detection in video surveillance for smart cities and public security. However, most current methods relied on single-scale information to extract appearance (spatial) features and lacked motion (temporal) continuity between video frames. This can cause a loss of partial spatiotemporal information that has great potential to predict future frames, affecting the accuracy of abnormality detection. Thus, we propose a novel prediction network to improve the performance of anomaly detection. Due to the objects of various scales in each video, we use different receptive fields to extract detailed appearance features by the hybrid dilated convolution (HDC) module. Meanwhile, the deeper bidirectional convolutional long short-term memory (DB-ConvLSTM) module can remember the motion information between consecutive frames. Furthermore, we use RGB difference loss to replace optical flow loss as temporal constraint, which greatly reduces the time for optical flow extraction. Compared with the state-of-the-art methods in the anomaly-detection task, experiments prove that our method can more accurately detect abnormalities in various video surveillance scenes.

A robust receptive field code for optic flow detection and decomposition during self-motion

The perception of optic flow is essential for any visually guided behavior of a moving animal. To mechanistically predict behavior and understand the emergence of self-motion perception in vertebrate brains, it is essential to systematically characterize the motion receptive fields (RFs) of optic flow processing neurons. Here, we present the fine-scale RFs of thousands of motion-sensitive neurons studied in the diencephalon and the midbrain of zebrafish. We found neurons that serve as linear filters and robustly encode directional and speed information of translation-induced optic flow. These neurons are topographically arranged in pretectum according to translation direction. The unambiguous encoding of translation enables the decomposition of translational and rotational self-motion information from mixed optic flow. In behavioral experiments, we successfully demonstrated the predicted decomposition in the optokinetic and optomotor responses. Together, our study reveals the algorithm and the neural implementation for self-motion estimation in a vertebrate visual system.

Gestures as scaffolding for L2 narrative recall: The role of gesture type, task complexity, and working memory

This study investigated the extent to which different pedagogical gestures contribute to learners’ foreign or second language (L2) narrative recall, and further discussed how task complexity and task difficulty (i.e. working memory capacity or WMC) influence recall performance. Sixty-four adolescent learners, assigned to four different gesture viewing conditions (iconic gestures, deictic gestures, beat gestures, or no gesture), were required to listen to an instructor telling two stories (one complex and one simple) and then retell both stories twice: once immediately after listening (immediate recall) and a second time two weeks later (delayed recall). Recall performance was evaluated by the number of relevant pieces of event and motion information produced in the participants’ retelling. The results show that L2 learners who were exposed to deictic and iconic gesture conditions outperformed the other gesture groups, particularly in delayed narrative recall, but only in complex tasks where cognitive demands were increased. It was also found that event and motion information was retained for a longer period of time in the deictic and iconic conditions respectively. Although both high and low WMC groups benefitted from viewing gestures, this finding further indicates that the beneficial effect of gestures on learners could possibly compensate for low WMC by providing scaffolding that reduces cognitive burden in narrative recall.