Object Detection and Tracking

Object Detection and Tracking combines spatial object localisation with temporal tracking to identify, classify, and follow objects across video frames or sensor streams.

Semantic Classification

Content

• Object Detection and Tracking combines spatial object localisation with temporal tracking to identify, classify, and follow objects across video frames or sensor streams. This capability is essential for autonomous systems to understand dynamic environments, predict object motion, and make safe navigation decisions. Modern systems employ deep learning detectors (YOLO, Faster R-CNN) combined with tracking algorithms (Kalman filters, SORT, DeepSORT).

• Products.Blog DeepDataSpace | The Go-To Choice for CV Data Visualization, Annotation, and Model Analysis

• Segment anything from Meta

• Automate Your Artistic Vision: Batch Inpainting Magic with DINO in Comfy! (youtube.com)

• facebookresearch/detectron2: Detectron2 is a platform for object detection, segmentation and other visual recognition tasks. (github.com)

• roboflow/supervision: We write your reusable computer vision tools. 💜 (github.com)

• The paper introduces SAM-PT, an extension of the Segment Anything Model (SAM) that combines tracking and segmentation in dynamic videos. SAM-PT uses sparse point selection and propagation techniques to generate masks, achieving strong zero-shot performance on popular video object segmentation benchmarks. Unlike traditional object-centric mask propagation strategies, SAM-PT utilizes point propagation to capture local structure information that is independent of object semantics. The paper also demonstrates the effectiveness of point-based tracking through evaluation on the Unidentified Video Objects (UVO) benchmark. To improve tracking accuracy, SAM-PT employs K-Medoids clustering for point initialization and tracks both positive and negative points to distinguish the target object. Additionally, multiple mask decoding passes and a point re-initialization strategy are used for mask refinement. The paper includes interactive video segmentation demos and showcases the results of SAM-PT on the DAVIS 2017 dataset, highlighting successful cases as well as failure cases. The effectiveness of SAM-PT is further demonstrated on avatar segmentation. The code and models for SAM-PT are available on GitHub. The paper concludes with a citation for reference.

• Segment and identify

• CodingMantras/yolov8-streamlit-detection-tracking: YOLOv8 object detection algorithm and Streamlit framework for Real-Time Object Detection and tracking in video streams. (github.com)

• YOLO detect anything

• yolo segment medium post

• Trainable segment anything (useful for museum collections?)

• Segment Anything, which can “cut out” any object in any image or video with a single click. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks.

• This repository contains code for the Painter and SegGPT models from the BAAI Vision Foundation. These models are designed for in-context visual learning, and can be used to segment images and generate descriptions of them.

• segmentation colours

• The text presents SegGPT, a generalist model for segmenting everything in context. The model is trained to unify various segmentation tasks into a generalist in-context learning framework, and is evaluated on a broad range of tasks, including few-shot semantic segmentation, video object segmentation, semantic segmentation, and panoptic segmentation. Results show strong capabilities in segmenting in-domain and out-of-domain targets, either qualitatively or quantitatively.

• Video-LLaMA is a project aimed at enhancing large language models (LLMs) with audio and visual understanding capabilities. It is built on top of BLIP-2 and MiniGPT-4 and consists of two core components: Vision-Language (VL) Branch and Audio-Language (AL) Branch. The VL Branch uses a two-layer video Q-Former and a frame embedding layer to compute video representations and is trained on the Webvid-2M video caption dataset with a video-to-text generation task, in addition to image-text pairs from LLaVA. The AL Branch, on the other hand, uses a two-layer audio Q-Former and an audio segment embedding layer to compute audio representations and is trained on video/image instrucaption data to connect the output of ImageBind to language decoder. The project provides pre-trained and fine-tuned checkpoints and users need to obtain them before using the repository. The repository also includes an example output and instructions on how to run the demo locally and how to perform the training. The project has been released under the BSD-3-Clause license. https://github.com/DAMO-NLP-SG/Video-LLaMA

• https://sam2.metademolab.com/ Segmentation and Identification

• https://go.fb.me/edcjv9

• Segmentation and Identification WebDev and Consumer Tooling Segment Anything WebGPU - a Hugging Face Space by Xenova

• ZhengPeng7/BiRefNet: [arXiv’24] Bilateral Reference for High-Resolution Dichotomous Image Segmentation (github.com) Segmentation and Identification

• Product Design Segmentation and Identification Image Generation

• Motion Inversion for Video Customization (wileewang.github.io) AI Video Segmentation and Identification Product Design

• Amshaker/MAVOS: Efficient Video Object Segmentation via Modulated Cross-Attention Memory (github.com) Segmentation and Identification

• Segmentation and Identification SC VD 103 (youtube.com) simple background removal

• Yolo guide Segmentation and Identification Human Pose SLAM Capture System Blog – YOLO Unraveled: A Clear Guide (opencv.ai)

• Efficient Segmentation and Identification for Hardware and Edge Paper page - TinySAM: Pushing the Envelope for Efficient Segment Anything Model (huggingface.co)

• Incredibly stable depth estimation from adobe

• Holistic segment unknowns

• Beyond bounding boxes

• Video to dataset (LAION)

• Products.Blog DeepDataSpace | The Go-To Choice for CV Data Visualization, Annotation, and Model Analysis

• Segment anything from Meta

• Automate Your Artistic Vision: Batch Inpainting Magic with DINO in Comfy! (youtube.com)

• facebookresearch/detectron2: Detectron2 is a platform for object detection, segmentation and other visual recognition tasks. (github.com)

• roboflow/supervision: We write your reusable computer vision tools. 💜 (github.com)

• The paper introduces SAM-PT, an extension of the Segment Anything Model (SAM) that combines tracking and segmentation in dynamic videos. SAM-PT uses sparse point selection and propagation techniques to generate masks, achieving strong zero-shot performance on popular video object segmentation benchmarks. Unlike traditional object-centric mask propagation strategies, SAM-PT utilizes point propagation to capture local structure information that is independent of object semantics. The paper also demonstrates the effectiveness of point-based tracking through evaluation on the Unidentified Video Objects (UVO) benchmark. To improve tracking accuracy, SAM-PT employs K-Medoids clustering for point initialization and tracks both positive and negative points to distinguish the target object. Additionally, multiple mask decoding passes and a point re-initialization strategy are used for mask refinement. The paper includes interactive video segmentation demos and showcases the results of SAM-PT on the DAVIS 2017 dataset, highlighting successful cases as well as failure cases. The effectiveness of SAM-PT is further demonstrated on avatar segmentation. The code and models for SAM-PT are available on GitHub. The paper concludes with a citation for reference.

• Segment and identify

• CodingMantras/yolov8-streamlit-detection-tracking: YOLOv8 object detection algorithm and Streamlit framework for Real-Time Object Detection and tracking in video streams. (github.com)

• YOLO detect anything

• yolo segment medium post

• Trainable segment anything (useful for museum collections?)

• Segment Anything, which can “cut out” any object in any image or video with a single click. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks.

• This repository contains code for the Painter and SegGPT models from the BAAI Vision Foundation. These models are designed for in-context visual learning, and can be used to segment images and generate descriptions of them.

• segmentation colours

• The text presents SegGPT, a generalist model for segmenting everything in context. The model is trained to unify various segmentation tasks into a generalist in-context learning framework, and is evaluated on a broad range of tasks, including few-shot semantic segmentation, video object segmentation, semantic segmentation, and panoptic segmentation. Results show strong capabilities in segmenting in-domain and out-of-domain targets, either qualitatively or quantitatively.

• Video-LLaMA is a project aimed at enhancing large language models (LLMs) with audio and visual understanding capabilities. It is built on top of BLIP-2 and MiniGPT-4 and consists of two core components: Vision-Language (VL) Branch and Audio-Language (AL) Branch. The VL Branch uses a two-layer video Q-Former and a frame embedding layer to compute video representations and is trained on the Webvid-2M video caption dataset with a video-to-text generation task, in addition to image-text pairs from LLaVA. The AL Branch, on the other hand, uses a two-layer audio Q-Former and an audio segment embedding layer to compute audio representations and is trained on video/image instrucaption data to connect the output of ImageBind to language decoder. The project provides pre-trained and fine-tuned checkpoints and users need to obtain them before using the repository. The repository also includes an example output and instructions on how to run the demo locally and how to perform the training. The project has been released under the BSD-3-Clause license. https://github.com/DAMO-NLP-SG/Video-LLaMA

• https://sam2.metademolab.com/ Segmentation and Identification

• https://go.fb.me/edcjv9

• Segmentation and Identification WebDev and Consumer Tooling Segment Anything WebGPU - a Hugging Face Space by Xenova

• ZhengPeng7/BiRefNet: [arXiv’24] Bilateral Reference for High-Resolution Dichotomous Image Segmentation (github.com) Segmentation and Identification

• Product Design Segmentation and Identification Image Generation

• Motion Inversion for Video Customization (wileewang.github.io) AI Video Segmentation and Identification Product Design

• Amshaker/MAVOS: Efficient Video Object Segmentation via Modulated Cross-Attention Memory (github.com) Segmentation and Identification

• Segmentation and Identification SC VD 103 (youtube.com) simple background removal

• Yolo guide Segmentation and Identification Human Pose SLAM Capture System Blog – YOLO Unraveled: A Clear Guide (opencv.ai)

• Efficient Segmentation and Identification for Hardware and Edge Paper page - TinySAM: Pushing the Envelope for Efficient Segment Anything Model (huggingface.co)

• Incredibly stable depth estimation from adobe

• Holistic segment unknowns

• Beyond bounding boxes

• Video to dataset (LAION)

  Core Characteristics

• Real-Time Detection: Frame-rate object identification

• Multi-Object Tracking: Simultaneous tracking of multiple entities

• Temporal Consistency: Maintenance of object identities across frames

• Occlusion Handling: Tracking through partial or full occlusions

• Motion Prediction: Trajectory forecasting for collision avoidance

  Relationships

• Component Of: Perception System

• Related: Computer Vision, Deep Learning, Kalman Filtering

• Algorithms: YOLO, Faster R-CNN, SORT, DeepSORT, Kalman Filter

  Key Literature

  1. Bewley, A., et al. (2016). “Simple online and realtime tracking.” ICIP, 3464-3468.

  2. Wojke, N., Bewley, A., & Paulus, D. (2017). “Simple online and realtime tracking with a deep association metric.” ICIP, 3645-3649.

  See Also

• Object Detection

• Computer Vision

• Perception System

  Core Characteristics

• Real-Time Detection: Frame-rate object identification

• Multi-Object Tracking: Simultaneous tracking of multiple entities

• Temporal Consistency: Maintenance of object identities across frames

• Occlusion Handling: Tracking through partial or full occlusions

• Motion Prediction: Trajectory forecasting for collision avoidance

  Relationships

• Component Of: Perception System

• Related: Computer Vision, Deep Learning, Kalman Filtering

• Algorithms: YOLO, Faster R-CNN, SORT, DeepSORT, Kalman Filter

  Key Literature

  1. Bewley, A., et al. (2016). “Simple online and realtime tracking.” ICIP, 3464-3468.

  2. Wojke, N., Bewley, A., & Paulus, D. (2017). “Simple online and realtime tracking with a deep association metric.” ICIP, 3645-3649.

  See Also

• Object Detection

• Computer Vision

• Perception System

Provenance