Collaborating Authors


Novel techniques extract more accurate data from images degraded by environmental factors


Computer vision technology is increasingly used in areas such as automatic surveillance systems, self-driving cars, facial recognition, healthcare and social distancing tools. Users require accurate and reliable visual information to fully harness the benefits of video analytics applications but the quality of the video data is often affected by environmental factors such as rain, night-time conditions or crowds (where there are multiple images of people overlapping with each other in a scene). Using computer vision and deep learning, a team of researchers led by Yale-NUS College Associate Professor of Science (Computer Science) Robby Tan, who is also from the National University of Singapore's (NUS) Faculty of Engineering, has developed novel approaches that resolve the problem of low-level vision in videos caused by rain and night-time conditions, as well as improve the accuracy of 3D human pose estimation in videos. The research was presented at the 2021 Conference on Computer Vision and Pattern Recognition (CVPR), a top ranked computer science conference. Night-time images are affected by low light and man-made light effects such as glare, glow, and floodlights, while rain images are affected by rain streaks or rain accumulation (or rain veiling effect).

Defeating Big Brother with Glasses: An Attack on Facial Recognition Neural Networks


As deep learning techniques continue to advance, image recognition systems are becoming more and more powerful. With this power comes great reward -- helping diagnose disease from x-rays and self-driving cars are just two examples. But there is also potential for harm, particularly concerning facial recognition. In the future, it's possible that surveillance cameras with state-of-the-art facial recognition technology could pop up on every street corner, effectively eliminating any privacy we still have. Fortunately, some researchers are already coming up with ways to counteract deep learning based facial recognition. I would like to highlight one interesting method -- using an adversarial attack in the form of specially colored glasses to confuse facial recognition algorithms.

Create Dataset for Computer Vision


The groundbreaking applications of Artificial intelligence are attracting tech multinationals like Apple, Microsoft, Amazon and Facebook to work on their future projects with more AI focused strategies. The AI effect is influencing the product road map of all such companies having the renowned AI-based applications that are launched at regular intervals in a year to automate their business operations with more promising results. Computer Vision is an important development under AI that has been extensively explored and applied into various industries from outdated to innovative self-driving cars moving on roads without human intervention. Such AI-backed innovative technologies work on such principles that encompass a huge amount of training data for computer vision. All these steps have their own challenges in terms of technical know-how and operational activities, so here we will discuss and help you how to deal with the labeling of training data and other related aspects required to complete this process. Before we start labeling of training data, you need aware where the technology of Computer Vision is effectively used to produce an AI-backed system or machine that can perform without too much human instructions and do their job independently as per the changing situations.

Implementing Real-time Object Detection System using PyTorch and OpenCV


The Self-Driving car might still be having difficulties understanding the difference between humans and garbage can, but that does not take anything away from the amazing progress state-of-the-art object detection models have made in the last decade. Combine that with the image processing abilities of libraries like OpenCV, it is much easier today to build a real-time object detection system prototype in hours. In this guide, I will try to show you how to develop sub-systems that go into a simple object detection application and how to put all of that together. I know some of you might be thinking why I am using Python, isn't it too slow for a real-time application, and you are right; to some extent. The most compute-heavy operations, like predictions or image processing, are being performed by PyTorch and OpenCV both of which use c behind the scene to implement these operations, therefore it won't make much difference if we use c or python for our use case here.

Multi-Object Tracking Metrics


The Evaluation process is one of the most important steps in build a Machine Learning Model. Especially when it comes to real-time detection plus tracking system. Computer Vision applications in tracking are becoming increasingly popular in surveillance, Sports Analytics, Autonomous vehicles, etc. So, evaluating your model would be the most difficult task before your deploy it. Today, let's go through a set of metrics that evaluates your tracking system and gives you a better understanding of your model.

Experimental Analysis of Trajectory Control Using Computer Vision and Artificial Intelligence for Autonomous Vehicles Artificial Intelligence

Perception of the lane boundaries is crucial for the tasks related to autonomous trajectory control. In this paper, several methodologies for lane detection are discussed with an experimental illustration: Hough transformation, Blob analysis, and Bird's eye view. Following the abstraction of lane marks from the boundary, the next approach is applying a control law based on the perception to control steering and speed control. In the following, a comparative analysis is made between an open-loop response, PID control, and a neural network control law through graphical statistics. To get the perception of the surrounding a wireless streaming camera connected to Raspberry Pi is used. After pre-processing the signal received by the camera the output is sent back to the Raspberry Pi that processes the input and communicates the control to the motors through Arduino via serial communication.

Composition and Application of Current Advanced Driving Assistance System: A Review Artificial Intelligence

Due to the growing awareness of driving safety and the development of sophisticated technologies, advanced driving assistance system (ADAS) has been equipped in more and more vehicles with higher accuracy and lower price. The latest progress in this field has called for a review to sum up the conventional knowledge of ADAS, the state-of-the-art researches, and novel applications in real-world. With the help of this kind of review, newcomers in this field can get basic knowledge easier and other researchers may be inspired with potential future development possibility. This paper makes a general introduction about ADAS by analyzing its hardware support and computation algorithms. Different types of perception sensors are introduced from their interior feature classifications, installation positions, supporting ADAS functions, and pros and cons. The comparisons between different sensors are concluded and illustrated from their inherent characters and specific usages serving for each ADAS function. The current algorithms for ADAS functions are also collected and briefly presented in this paper from both traditional methods and novel ideas. Additionally, discussions about the definition of ADAS from different institutes are reviewed in this paper, and future approaches about ADAS in China are introduced in particular.

Risk Ranked Recall: Collision Safety Metric for Object Detection Systems in Autonomous Vehicles Artificial Intelligence

Abstract--Commonly used metrics for evaluation of object detection systems (precision, recall, mAP) do not give complete information about their suitability of use in safety critical tasks, like obstacle detection for collision avoidance in Autonomous Vehicles (AV). Ranks are assigned based on an objective cyber-physical model for the risk of collision. Recall is measured for each rank. A front view scene from BDD100K [1] dataset with 4 labeled vehicles. Intuitively, the closer vehicles are more important to detect than those farther away.

Semantically Controllable Scene Generation with Guidance of Explicit Knowledge Artificial Intelligence

Deep Generative Models (DGMs) are known for their superior capability in generating realistic data. Extending purely data-driven approaches, recent specialized DGMs may satisfy additional controllable requirements such as embedding a traffic sign in a driving scene, by manipulating patterns \textit{implicitly} in the neuron or feature level. In this paper, we introduce a novel method to incorporate domain knowledge \textit{explicitly} in the generation process to achieve semantically controllable scene generation. We categorize our knowledge into two types to be consistent with the composition of natural scenes, where the first type represents the property of objects and the second type represents the relationship among objects. We then propose a tree-structured generative model to learn complex scene representation, whose nodes and edges are naturally corresponding to the two types of knowledge respectively. Knowledge can be explicitly integrated to enable semantically controllable scene generation by imposing semantic rules on properties of nodes and edges in the tree structure. We construct a synthetic example to illustrate the controllability and explainability of our method in a clean setting. We further extend the synthetic example to realistic autonomous vehicle driving environments and conduct extensive experiments to show that our method efficiently identifies adversarial traffic scenes against different state-of-the-art 3D point cloud segmentation models satisfying the traffic rules specified as the explicit knowledge.

XAI Method Properties: A (Meta-)study Artificial Intelligence

In the meantime, a wide variety of terminologies, motivations, approaches and evaluation criteria have been developed within the scope of research on explainable artificial intelligence (XAI). Many taxonomies can be found in the literature, each with a different focus, but also showing many points of overlap. In this paper, we summarize the most cited and current taxonomies in a meta-analysis in order to highlight the essential aspects of the state-of-the-art in XAI. We also present and add terminologies as well as concepts from a large number of survey articles on the topic. Last but not least, we illustrate concepts from the higher-level taxonomy with more than 50 example methods, which we categorize accordingly, thus providing a wide-ranging overview of aspects of XAI and paving the way for use case-appropriate as well as context-specific subsequent research.