Vehicle Detection Based on Visual Saliency and Deep Sparse Convolution Hierarchical Model

Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification.These types of methods generally have high processing times and low vehicle detection performance.To address this issue,a visual saliency and deep sparse convolution hierarchical model based vehicle detection algorithm is proposed.A visual saliency calculation is firstly used to generate a small vehicle candidate area.The vehicle candidate sub images are then loaded into a sparse deep convolution hierarchical model with an SVM-based classifier to perform the final detection.The experimental results demonstrate that the proposed method is with 94.81% correct rate and 0.78% false detection rate on the existing datasets and the real road pictures captured by our group,which outperforms the existing state-of-the-art algorithms.More importantly,high discriminative multi-scale features are generated by deep sparse convolution network which has broad application prospects in target recognition in the field of intelligent vehicle.

Robust Local Light Field Synthesis via Occlusion-aware Sampling and Deep Visual Feature Fusion

Novel view synthesis has attracted tremendous research attention recently for its applications in virtual reality and immersive telepresence.Rendering a locally immersive light field(LF)based on arbitrary large baseline RGB references is a challenging problem that lacks efficient solutions with existing novel view synthesis techniques.In this work,we aim at truthfully rendering local immersive novel views/LF images based on large baseline LF captures and a single RGB image in the target view.To fully explore the precious information from source LF captures,we propose a novel occlusion-aware source sampler(OSS)module which efficiently transfers the pixels of source views to the target view′s frustum in an occlusion-aware manner.An attention-based deep visual fusion module is proposed to fuse the revealed occluded background content with a preliminary LF into a final refined LF.The proposed source sampling and fusion mechanism not only helps to provide information for occluded regions from varying observation angles,but also proves to be able to effectively enhance the visual rendering quality.Experimental results show that our proposed method is able to render high-quality LF images/novel views with sparse RGB references and outperforms state-of-the-art LF rendering and novel view synthesis methods.

VISHIEN-MAAT:Scrollytelling visualization design for explaining Siamese Neural Network concept to non-technical users

The past decade has witnessed rapid progress in AI research since the breakthrough in deep learning.AI technology has been applied in almost every field;therefore,technical and non-technical endusers must understand these technologies to exploit them.However existing materials are designed for experts,but non-technical users need appealing materials that deliver complex ideas in easy-tofollow steps.One notable tool that fits such a profile is scrollytelling,an approach to storytelling that provides readers with a natural and rich experience at the reader’s pace,along with in-depth interactive explanations of complex concepts.Hence,this work proposes a novel visualization design for creating a scrollytelling that can effectively explain an AI concept to non-technical users.As a demonstration of our design,we created a scrollytelling to explain the Siamese Neural Network for the visual similarity matching problem.Our approach helps create a visualization valuable for a shorttimeline situation like a sales pitch.The results show that the visualization based on our novel design helps improve non-technical users’perception and machine learning concept knowledge acquisition compared to traditional materials like online articles.

Distance-directed Target Searching for a Deep Visual Servo SMA Driven Soft Robot Using Reinforcement Learning

Performing complex tasks by soft robots in constrained environment remains an enormous challenge owing to the limitations of flexible mechanisms and control methods.In this paper,a novel biomimetic soft robot driven by Shape Memory Alloy(SMA)with light weight and multi-motion abilities is introduced.We adapt deep learning to perceive irregular targets in an unstructured environment.Aiming at the target searching task,an intelligent visual servo control algorithm based on Q-leaming is proposed to generate distance-directed end effector locomotion.In particular,a threshold reward system for the target searching task is proposed to enable a certain degree of tolerance for pointing errors.In addition,the angular velocity and working space of the end effector with load and without load based on the established coupling kinematic model are presented.Our framework enables the trained soft robot to take actions and perform target searching.Realistic experiments under different conditions demonstrate the convergence of the learning process and effectiveness of the proposed algorithm.