Self-driving cars and delivery robots are set to shape the future of transportation, but they still have to learn how to co-exist with humans in close proximity. Autonomous systems need to detect pedestrians and understand the meaning of their actions before making appropriate decisions in response. Action recognition is therefore an essential task for transportation applications, and yet very challenging, as there is no control over the distances of pedestrians or the real-world variations like lighting, weather, and occlusions.
During my internship at Hesai Tech, I worked on the point cloud-based object detection problem. This picture is from Waymo Open Dataset, and I developed several detector with OpenPCDet. One of the challenges is the scale of Waymo Open Dataset (2TB), and I’ve managed to process the whole dataset with a divide and conquer scheme. The focus of my project was to merge the information from multiple frames, so as to raise the overall accuracy of detection.
The Deep Learning course in Tongji University (2019 FALL) contains the following contents:
Machine learning basics: Linear regression and classification, Logistic regression for multi-class Classification problems.
Artificial Neural Networks: Fully-Connected Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks.
Applications of Deep Learning: Image Classification, Transfer Learning, Style Transfer, Object Detection, Segmentation, GAN
Assignments include:
Linear Regression with Numpy
Logistic Regression with Numpy
Multi-class Logistic Regression with Numpy