Global Data Association for Multiple Pedestrian Tracking
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.
Visual Analysis of Extremely Dense Crowded Scenes
Analyzing dense crowd scenes using computer vision techniques for counting, detecting, and tracking individuals. Explores multiple data sources, context, and neighborhood motion to address challenges in extremely crowded environments.
Human Detection, Tracking and Segmentation in Surveillance Video
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 Temporarily Untrimmed Videos
Action recognition in untrimmed videos: challenges, datasets, and annotation techniques for improving accuracy in real-world scenarios with ambiguous content and varying video qualities.
Statistical and Spatial Consensus Collection for Detector Adaption
Explore detector adaptation techniques for improved pedestrian detection across domains, focusing on statistical and spatial consensus methods to enhance accuracy and generalization.
Contextual Image Understanding and Scene Identification in Videos - Thesis Defense
Explore image understanding using context, focusing on scene identification and object detection in videos through innovative techniques like Top-Down Visual Tree representation and Random Walk incorporation.
Instance Segmentation - Lecture 26
Explore instance segmentation in computer vision, covering Mask R-CNN, ROI pooling, and alignment techniques for precise object detection and pixel-level classification.
Image Segmentation Basics and Techniques - Lecture 23
Explore image segmentation basics, including binarization, thresholding, and region-based techniques. Learn algorithms, visualization methods, and practical applications in computer vision.
Object Detection in Computer Vision - Lecture 22
Explore advanced object detection techniques in computer vision, including R-CNN, Fast R-CNN, and Faster R-CNN. Learn about training processes, region proposals, and improvements in detection algorithms.
Object Detection in Computer Vision - Lecture 21
Explore advanced object detection techniques, including precision-recall metrics, mean average precision, and multitask networks for computer vision applications.
Object Detection Techniques - Part I - Lecture 20
Explore object detection techniques including sliding windows, pyramid construction, feature extraction, and post-processing. Learn about evaluation metrics like IoU, precision, and recall for assessing detection accuracy.
Classification in Computer Vision - Lecture 18
Explore classification in computer vision, covering features, linear classification, nearest neighbor, decision boundaries, and maximum margin classifiers. Gain insights into fundamental algorithms and problem-solving approaches.
Introduction to Convolutional Neural Networks - Part I - Lecture 8
Introduction to convolutional neural networks, covering architecture, applications in computer vision and natural language processing, with examples like AlexNet and super-resolution techniques.
Computer Vision - Administrative and Course Overview - Lecture 1
Introduction to graduate-level computer vision, covering mathematical foundations, deep learning applications, and advanced topics like object detection and image segmentation.
Podman AI Lab: Building AI Applications with Local LLMs
Explore the features of Podman AI Lab, an extension of Podman Desktop, in this 29-minute conference talk from Devoxx. Discover sample AI applications (recipes) from the built-in catalog, including chatbots, audio-to-text conversion, and object detection.…
Be My Eyes - Leveraging Generative AI to Help the Blind and Visually Impaired
Explore how AI and computer vision can narrate the world for visually impaired individuals, transforming visual content into real-time audio descriptions.
Annotate Images Like a Pro - Python Image Annotation Tool Demo
Master Python-based image annotation with a comprehensive tool for 2D and multi-dimensional images. Learn manual and semi-automatic techniques, export options, and additional features for efficient image analysis and model training.
YOLO11: Performance Benchmark and Real World Use Cases
Explore YOLO11's performance benchmarks, compare it to previous models, and learn to build real-world applications using this cutting-edge object detection technology.
How I Combined 3 Powerful AI Models to Automate Image Editing
Explore AI-driven image processing using Grounding DINO, SAM, and Stable Diffusion. Learn to detect, segment, and generate new images with step-by-step guidance on model integration and custom prompts.
TensorFlow Object Detection API
Learn to implement and customize TensorFlow's Object Detection API for real-time video analysis and tracking of custom objects through hands-on tutorials.
Tensorflow Object Detection
Learn to set up and use TensorFlow Object Detection API with TensorFlow 2.0 on Windows, including custom object detection and real-time image segmentation from video streams.