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Learn Object Detection, earn certificates with paid and free online courses from Stanford, Alexander Amini, Johns Hopkins, UC Berkeley and other top universities around the world. Read reviews to decide if a class is right for you.
Learn how to locate an object and track it over time. These techniques are used in a variety of moving systems, such as self-driving car navigation and drone flight.
Build an Object Detection Model from Scratch using Deep Learning and Transfer Learning
Explore object tracking and motion detection techniques using MATLAB. Learn to apply deep neural networks, optical flow, and advanced algorithms for real-world computer vision applications.
Learn to train Object Detection Transformers using DETR, from environment setup to custom dataset training and model evaluation. Covers PyTorch, COCO datasets, and PyTorch Lightning for efficient deep learning workflows.
Explore NVIDIA DeepStream's multi-object tracker options, architecture, and optimization strategies for AI-based vision applications, with focus on NvDCF tracker and configuration parameters.
Explore action-driven object tracking using deep reinforcement learning, covering policy, value functions, and training approaches for improved visual tracking performance.
Explore advanced data association techniques for multi-target tracking, from GMCP to GMMCP, and a novel approach for crowd tracking using Binary Quadratic Programming and Modified Frank-Wolfe algorithm.
Explore advanced techniques for human detection, tracking, and segmentation in surveillance videos, focusing on scene-specific learning and occlusion handling for improved accuracy and efficiency.
Action recognition in untrimmed videos: challenges, datasets, and annotation techniques for improving accuracy in real-world scenarios with ambiguous content and varying video qualities.
Explore detector adaptation techniques for improved pedestrian detection across domains, focusing on statistical and spatial consensus methods to enhance accuracy and generalization.
Explore object tracking in computer vision, covering algorithms, datasets, and challenges. Learn about various tracking methods and their performance in different scenarios.
Explore visual material recognition through generative and discriminative approaches, focusing on reflectance models, material traits, and their applications in computer vision.
Explore object recognition techniques, from Bag of Words to selective search, covering codebooks, visual synonyms, and object localization for effective image classification.
Explore recent advancements in online object tracking, covering sparsity-based classifiers, occlusion handling, and evaluation methodologies for improved visual tracking algorithms.
Object detection can be very useful in mundane tasks. This course will teach you how to build an object detection model from beginning to end for real-life use cases, suitable for using on a daily basis.
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