暗光增强主要解决的是夜晚或光线暗区域拍摄的图像导致人眼或机器“看不清”暗光区域的场景，希望设计基于神经网络的end to end模型，通过暗光增强技术处理暗光图片，输出清晰图片，并在公开测试集上，可以达到最新业界水平。在实际测试集上，经过裁剪优化满足技术落地需求。
图像去模糊一直是图像处理中困扰业界的难题。图像模糊产生的原因可能非常复杂。比如，相机晃动，失焦，拍摄物体高速运动等等。期待希望设计基于神经网络的end to end模型，通过图像增强技术处理模糊图像，输出清晰图片，并在公开测试集上，可以达到最新业界水平。在实际测试集上，经过裁剪优化满足技术落地需求。
3. Multi-PersonAbnormal Behavior Detection
Computer Vision, Instance Segmentation, Human Key-PointDetection (Pose Estimation, HumanSkeletal System Key-PointDetection), Action Recognition
As techniques in machine learning and computer vision are developing rapidly, a wide crosscity network of cameras has been set up to construct a powerful surveillance system which guarantees social security. Using this camera network in conjunction with advanced image analysis techniques, a powerful system can be built to recognize passengers and their actions, thus to discover abnormal human behavior in certain environments such as elevators or vehicles, and warning alarms might be triggered subsequently. This can reduce accidents and protect the safety of people’s lives and property.
1. Instance Segmentation
The prediction of future events is an important feature of intelligent behavior, and image prediction is one task falling under such goals. Recent work shows that the semantic prediction of future frames for semantic segmentation is more effective than performing prediction of RGB frames and segmentation separately. Therefore, it is promising to explore a new solution for instance segmentation to alleviate conflicting predictions in a cabin environment.
2. Human Skeletal System KeyPointDetection
Human skeletal system keypoint detection plays an importance role in describing human pose and predicting human behavior, which is the foundation of many computer vision tasks such as action recognition, abnormal behavior detection, and autonomous driving. Through the detection and analysis of key-points, abnormal behavior recognition and prediction can be leveraged to guarantee the safety of passengers.
3. Predicting Conflicts
Predicting conflicts through machine learning is becoming a popular topic in the intersecting fields of sociology and computer sciences. Due to the uncertainty and variability of factors that lead to conflict, conflict prediction is still very controversial within the academic field. With the increase of data resources and computational power, image analysis techniques can be used to predict the probability of conflict between drivers and passengers.
4. Image Enhancement
Computer Vision, Image Enhancement, Image Deblurring
Images and videos are playing an increasingly important role in the field of public safety. More and more surveillance cameras are installed in cities to ensure public safety. These cameras are installed in corporate environments, living quarters, markets, and individual locales etc. Due to the myriad of affecting factors in the shooting environment and the imaging quality of the imaging device, the generated images and videos are often blurry and unclear, which imposes great difficulty and challenge for subsequent image recognition. Blurring can be caused by many factors, such as insufficient night light, low resolution of imaging equipment, extreme weather conditions (rain and fog, etc.), excessive exposure of images/video, inaccurate camera focus, rapid movement of objects, etc.
To solve the above key issues, Our research objectives include but are not limited to thefollowing:
1.Dark Image Enhancement
Dark image enhancement mainly resolves scenes where the image is filmed at night or in a dark area. We plan to design an end-to-end model based on deep neural networks to generate clear images through dark image enhancement technologies, and ultimately achieve state-of-the-art performance on open image datasets.The technologies will satisfy all requirements in practical test datasets.
Image blurring has been a persistent issue in the field of image processing. The reasons for image blurring run a wide gamut of complex factors, such as camera shake, imprecise focus, high speed movement of objects, and so on. We plan to design an endtoend model based on deep neural networks to generate clear images through image enhancement on blurred images, and ultimately achieve state-of-the-art performance on open image datasets.In the practical test datasets.The technologies will satisfy all requirements in practical test datasets.