Video Annotation Services
Unlock the Full Potential of Your Vision AI with Precision Video Annotation
Video is the richest and most complex data type in computer vision and the hardest to annotate well. At NextWealth, we specialise in delivering precise, temporally consistent video annotations that train smarter, more reliable AI models across autonomous vehicles, healthcare, surveillance, sports analytics, and large language model development.
Our Human-in-the-Loop (HITL) approach ensures that object tracking, action recognition, frame-level segmentation, and temporal labelling are executed with the contextual accuracy and motion-level precision that fully automated tools cannot sustain. From dashcam and egocentric video to long-form broadcast footage and short-clip training data for multimodal LLMs, we annotate across every video format your AI pipeline demands be it at production scale, with defined frame rate specifications and SLA-backed turnaround.
Types of Video Annotation Services
We make use of efficient and convenient video data annotation methods that can be easily adapted to any machine-learning models. Our video annotation services enable the detection of all objects of interest frame-by-frame and make them recognizable to AI models through appropriate classification.
Video Classification
We categorise video clips and sequences by content type, activity, scene, or even assigning class labels at the clip level or across defined time segments. Video classification is used to train models for content moderation, media indexing, sports event detection, and activity recognition at scale. We handle both short-clip classification (seconds-long segments for model training) and long-form video classification (broadcast footage, surveillance recordings, medical procedure videos) , operationally distinct workflows that require different tooling, quality checks, and annotator skill sets.
Video Object Tracking
We annotate and track all objects of interest across video frames, maintaining consistent object identities and bounding box annotations through motion, occlusion, and re-entry. Our tracking annotation supports interpolation-assisted labelling for efficiency on long sequences, with frame-by-frame human validation at key transition points scene cuts, occlusion events, and re-identification moments where automated tracking most commonly fails. Frame rate and temporal precision are specified per project: high-speed automotive annotation typically requires per-frame annotation at 30 60 fps, while surveillance annotation may use keyframe-plus-interpolation workflows at lower frame rates.
Semantic & Instance Segmentation
We perform pixel-level and instance-level segmentation across video sequences, assigning class labels to every pixel in each frame and distinguishing between individual object instances of the same class. This is the annotation standard for autonomous driving scene understanding, medical video analysis, and industrial inspection use cases where the full geometry and boundaries of objects matter, not just their location.
Action & Activity Recognition
We label human and object actions across temporal windows tagging the start and end of specific activities, classifying motion types, and annotating fine-grained action sub-steps. Our action annotation supports pose-linked activity labelling (combining keypoint and action tags) for sports AI, rehabilitation monitoring, security anomaly detection, and workplace safety systems.
Keypoint & Pose Animation
We mark semantically meaningful points like body joints, facial landmarks, hand positions, vehicle control points across video frames, maintaining skeletal consistency through motion and partial occlusion. Keypoint video annotation is used to train pose estimation, gesture recognition, gait analysis, and biomechanical AI systems, with frame-level precision maintained across the full sequence.
Dashcam & Egocentric Video Annotation
Dashcam and egocentric (first-person perspective) video presents unique annotation challenges: rapid ego-motion, wide-angle lens distortion, variable lighting, and continuous scene change. NextWealth has dedicated annotation workflows for:
- Dashcam footage lane boundary detection, vehicle and pedestrian tracking, traffic sign recognition, near-miss event tagging, and ADAS event labelling across front, rear, and multi-camera configurations
- Egocentric video object interaction labelling, hand and tool tracking, gaze-relevant region annotation, and activity segmentation from a first-person viewpoint critical for AR/VR, wearable AI, and embodied intelligence research
Dashcam and egocentric annotation require annotators trained in perspective-aware labelling and ego-motion-aware tracking a distinct skill set from standard third-person video annotation.
Video Captioning & LLM Training Data
Video captioning annotation generates natural language descriptions of video content at the clip level, scene level, or frame level to train multimodal large language models (LLMs) and video-language models. This is a rapidly growing annotation category as foundation models increasingly require video understanding capability.
NextWealth supports:
- Dense video captioning timestamped natural language descriptions of events and actions throughout a video
- Clip-level captioning concise descriptions of short video segments for vision-language model pre-training
- Question-answer pair annotation generating and validating video QA pairs for multimodal model evaluation
- Video-text alignment annotation matching video segments to corresponding textual descriptions for contrastive training (CLIP-style video models)
Our multilingual annotators also support video captioning in Indian and global languages enabling diverse, language-rich training datasets for multilingual video-language models.
Long-Form vs. Short-Clip Annotation
These are operationally distinct annotation workflows and we staff and QA them accordingly:
Short-clip annotation (typically 1 30 seconds) is used for action recognition training, model evaluation benchmarks, and LLM training data. It prioritises dense, precise labelling within a contained temporal window, with high annotator throughput per hour.
Long-form annotation (minutes to hours broadcast video, surgical recordings, dashcam journeys, CCTV footage) requires sustained temporal consistency, re-identification logic across scene transitions, and structured keyframe-plus-interpolation workflows to maintain quality without unsustainable per-frame annotation cost. Long-form annotation also requires stricter QA protocols given the volume of frames and the compounding effect of early annotation errors across a long sequence.
Technical Specifications
| Parameter | Specification |
|---|---|
| Supported frame rates | 5 fps (surveillance) to 120 fps (high-speed sports/automotive) |
| Temporal precision | Per-frame, keyframe + interpolation, or segment-level defined per project |
| Annotation formats | COCO, MOT (Multi-Object Tracking), Pascal VOC, YOLO, custom JSON |
| Video formats | MP4, AVI, MOV, MKV, DICOM video (medical), proprietary dashcam formats |
| Annotation tooling | CVAT, Labelbox, Scale AI, Supervisely, Label Studio, client-proprietary platforms |
| QA layers | AI pre-check → senior reviewer → temporal consistency audit → client QA |
| Accuracy benchmark | 95–99%+ spatial accuracy; >95% temporal consistency across tracked sequences |
| Turnaround SLA | Defined per project; standard batches typically 24–72 hours |
Healthcare & Medical AI
We annotate surgical procedure recordings, endoscopy footage, ultrasound video, and patient monitoring streams labelling instrument positions, anatomical landmarks, procedural steps, and anomaly events. Video annotation for medical AI requires domain-trained annotators and strict data security protocols; NextWealth provides both, with annotation conducted in access-controlled environments aligned with healthcare data handling standards.
Sports AI & Performance Analytics
We annotate player movement, joint positions, ball tracking, event recognition, and tactical patterns across broadcast and multi-angle sports footage. Our keypoint and action annotation workflows support performance AI, coaching intelligence, broadcast enhancement, and injury prevention systems with short-clip annotation optimised for rapid model iteration and long-form annotation for full-match analysis.
Driver Monitoring Systems
We label in-cabin video for driver gaze direction, head pose, facial action units, drowsiness indicators, and distraction events training AI systems that issue real-time safety warnings to prevent accidents. This requires high temporal precision and frame-level annotation at 30+ fps to capture the sub-second behavioural cues that driver monitoring models depend on.
Security & Surveillance AI
We annotate CCTV and IP camera footage for individual tracking, anomaly detection, crowd behaviour analysis, intrusion recognition, and perimeter monitoring. Our long-form annotation workflows handle continuous surveillance recordings with sustained temporal consistency identifying and flagging events across hours of footage without annotation drift.
Manufacturing & Industrial Automation
We annotate production line video for defect detection, component placement verification, robot guidance, and process anomaly identification. Temporal annotation enables AI systems to detect not just static defects but process errors that only manifest across a sequence of frames missed by single-image inspection systems.
Media, News & Content AI
We provide training datasets for automated video transcription, fake news detection, content indexing, and highlight extraction in broadcast and digital media. Video captioning annotation supports AI systems that automatically generate descriptions, subtitles, and structured metadata for large video libraries.
Autonomous Vehicles & ADAS
We annotate dashcam and multi-sensor video for object detection, lane recognition, pedestrian tracking, traffic event labelling, and in-cabin driver monitoring. Our ADAS annotation covers emergency braking triggers, driver drowsiness events, blind spot warnings, and near-miss classification across front-facing, rear-facing, and 360° camera configurations. Temporal precision is maintained to the frame level at 30 60 fps to meet automotive perception stack requirements.
AR/VR & Embodied AI
Egocentric video annotation supports hand tracking, object interaction labelling, spatial scene understanding, and gaze-relevant region annotation for augmented reality applications, wearable AI devices, and embodied intelligence research programmes.
NextWealth’s Approach to High-Quality Video Annotation
We follow a structured, precision-first annotation process designed for temporal accuracy at scale.
Project Scoping
Frame rate requirements, temporal precision specifications, annotation type, and quality benchmarks are defined upfront.
Ontology Design
Class hierarchies, tracking logic, and edge-case handling rules are established before annotation begins.
AI-Assisted Pre-Annotation
Automated tools generate first-pass annotations, while human annotators validate, correct, and refine the results.
Temporal Quality Assurance
Dedicated quality reviews verify tracking consistency, re-identification accuracy, and label coherence across frame sequences.
Client Review Layer
A structured sample review and feedback loop is completed before proceeding with full-batch delivery.
Active Learning Integration
Model-flagged, low-confidence frames are routed back for human re-annotation to continuously improve model performance.
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NextWealth’s hard work and dedication are truly making a difference, streamlining our processes significantly. We really appreciate it!
My experience with NextWealth has been wonderful. The diligent team consistently delivers on time with a focus on quality. Their innovation-driven mindset fosters a win-win situation for both teams.
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