Visual Tracking Using an Insect Vision Embedded Particle Filter

Particle filtering (PF) based object tracking algorithms have drawn great attention from lots of scholars. The core of PF is to predict the possible location of the target via the state transition model. One commonly adopted approach is resorting to prior motion cues under the smooth motion assumption, which performs well when the target moves with a relatively stable velocity. However, it would possibly fail if the target is undergoing abrupt motion. To address this problem, inspired by insect vision, we propose a simple yet effective visual tracking framework based on PF. Utilizing the neuronal computational model of the insect vision, we estimate the motion of the target in a novel way so as to refine the position state of propagated particles using more accurate transition mode. Furthermore, we design a novel sample optimization framework where local and global search strategies are jointly used. In addition, we propose a new method to monitor long duration severe occlusion and we could recover the target. Experiments on publicly available benchmark video sequences demonstrate that the proposed tracking algorithm outperforms the state-of-the art methods in challenging scenarios, especially for tracking target which is undergoing abrupt motion or fast movement.</jats:p

Iterative Geometry-Aware Cross Guidance Network for Stereo Image Inpainting

Currently, single image inpainting has achieved promising results based on deep convolutional neural networks. However, inpainting on stereo images with missing regions has not been explored thoroughly, which is also a significant but different problem. One crucial requirement for stereo image inpainting is stereo consistency. To achieve it, we propose an Iterative Geometry-Aware Cross Guidance Network (IGGNet). The IGGNet contains two key ingredients, i.e., a Geometry-Aware Attention (GAA) module and an Iterative Cross Guidance (ICG) strategy. The GAA module relies on the epipolar geometry cues and learns the geometry-aware guidance from one view to another, which is beneficial to make the corresponding regions in two views consistent. However, learning guidance from co-existing missing regions is challenging. To address this issue, the ICG strategy is proposed, which can alternately narrow down the missing regions of the two views in an iterative manner. Experimental results demonstrate that our proposed network outperforms the latest stereo image inpainting model and state-of-the-art single image inpainting models.Comment: Accepted by IJCAI 202

ELECTRICITY GENERATION CHARACTERISTICS OF AN ANAEROBIC FLUIDIZED BED MICROBIAL FUEL CELL

Anaerobic fluidized bed microbial fuel cell (AFBMFC) was developed to investigate the effect of fluidization behaviors on the electrogenesis capacity. Waste water and active carbon were used as liquid and solid phase, respectively. The fuel cell was started up successfully using anaerobic activated sludge as inoculums. The power density is increased with increasing circular liquid velocity up to 450 mW·m-2. High COD remove rate reached 93% after five days operation. Meanwhile, the effects of cathode area on the electrogenesis capacity of AFB MFC were also investigated

Abrupt Motion Tracking via Nearest Neighbor Field Driven Stochastic Sampling

Stochastic sampling based trackers have shown good performance for abrupt motion tracking so that they have gained popularity in recent years. However, conventional methods tend to use a two-stage sampling paradigm, in which the search space needs to be uniformly explored with an inefficient preliminary sampling phase. In this paper, we propose a novel sampling-based method in the Bayesian filtering framework to address the problem. Within the framework, nearest neighbor field estimation is utilized to compute the importance proposal probabilities, which guide the Markov chain search towards promising regions and thus enhance the sampling efficiency; given the motion priors, a smoothing stochastic sampling Monte Carlo algorithm is proposed to approximate the posterior distribution through a smoothing weight-updating scheme. Moreover, to track the abrupt and the smooth motions simultaneously, we develop an abrupt-motion detection scheme which can discover the presence of abrupt motions during online tracking. Extensive experiments on challenging image sequences demonstrate the effectiveness and the robustness of our algorithm in handling the abrupt motions.Comment: submitted to Elsevier Neurocomputin

The Static and Dynamic Sensitivity of Magnetostrictive Bioinspired Whisker Sensor

Magnetostrictive bioinspired whisker is a new kind of sensor that can realize tactile and flow sensing by utilizing magnetoelastic effect. The sensitivity is a key technical indicator of whisker sensor. The paper presented a new magnetostrictive whisker based on Galfenol cantilever beam, as well as its operation principle. Then, the static and dynamic sensitivity of the whisker sensor was investigated by using a self-made experimental system. The results illustrated that the proposed sensor has a high sensitivity. Its static sensitivity is 2.2 mV/mN. However, its dynamic sensitivity depends on the vibration frequency. When working at the natural frequency of the cantilever beam, the dynamic sensitivity performs an obvious increase—1.3 mV/mN at 3.5 Hz (the first-order natural frequency) and 2.1 mV/mN at 40 Hz (the second-order natural frequency), respectively