672 Open source projects that are alternatives of or similar to pcan

[ECCV'20] Ocean: Object-aware Anchor-Free Tracking

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Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple Objects

Python tools for working with PedX dataset.

SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation (ECCV2020)

[NeurIPS'21] Unified tracking framework with a single appearance model. It supports Single Object Tracking (SOT), Video Object Segmentation (VOS), Multi-Object Tracking (MOT), Multi-Object Tracking and Segmentation (MOTS), Pose Tracking, Video Instance Segmentation (VIS), and class-agnostic MOT (e.g. TAO dataset).

MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects

Collection of papers, datasets, code and other resources for object tracking and detection using deep learning

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This repository contains the code for our fast polygonal building extraction from overhead images pipeline.

C++ Implementation of IOU Tracker presented in AVSS17

地理编码技术，提供地址标准化和相似度计算。

Neural Network for Dying Hair💈

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Target detection in the first frame and Tracking target by SiamRPN.

Easy multiple sclerosis white matter lesion segmentation using convolutional deep neural networks.

Dictionary Learning for image processing

[CVPR 2021] Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion. Semi-supervised VOS as well!

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No description or website provided.

A basic framework for pulmonary nodule detection and characterization in CT

Medical Image Segmentation Toolkit based on PaddlePaddle - 基于paddle的医学影像分割框架

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Data from the largest and longest measurement of online tracking.

Smart India Hackathon 2019 project given by the Department of Atomic Energy

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Javascript/WebGL lightweight face tracking library designed for augmented reality webcam filters. Features : multiple faces detection, rotation, mouth opening. Various integration examples are provided (Three.js, Babylon.js, FaceSwap, Canvas2D, CSS3D...).

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Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network