Solution - Computer Vision

Video Annotation

Object tracking with consistent IDs across frames, action recognition, temporal segmentation, and AV scenario annotation. Temporal interpolation reduces frame-by-frame work by 70%. ByteTrack multi-object tracking for high-density scenes. Supporting autonomous vehicles, surveillance, healthcare, and action recognition applications.

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70%
Reduction in frame-by-frame work via temporal interpolation between keyframes
ByteTrack
State-of-the-art multi-object tracking for consistent IDs across frames
30fps
Support for high-framerate video for automotive, sports, and security footage
ID-consistent
Track IDs maintained across occlusions, re-entries, and scene cuts
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Object TrackingAction RecognitionTemporal SegmentationByteTrackKeyframe AnnotationTemporal InterpolationAV ScenariosActivity DetectionObject TrackingAction RecognitionTemporal SegmentationByteTrack
Video Timeline Annotation
● CAR · ID-04 · 847 frames
● PEDESTRIAN · ID-07 · 312 frames
● BUS · ID-02 · 1,204 frames
● TWO-WHEELER · ID-11 · 526 frames
▼ OBJECT TRACKING TIMELINE
CAR-04
PED-07
BUS-02
MOT-11
0:00 2:16 4:32 ▶
✓ 23 OBJECTS · 4,832 FRAMES · QA PASS
What It Is

Object tracking with consistent identity across frames

Video annotation extends image annotation into the temporal dimension. The core challenge is not just labeling what is in each frame it is maintaining label consistency across frames as objects move, overlap, partially leave frame, and re-enter. Getting this right requires a combination of smart automated tracking and careful human review of edge cases that break automated tracking.

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Live Annotation Interface

Video Timeline Segment Annotation Tool

Annotators label temporal segments across video, audio, and action streams building datasets for video understanding, action recognition, and multimodal AI training.

ConcaveLabel Studio - Video Annotation · Clip: #8,204 · Duration: 4m 32s · Annotator: Meena R.
AUDIO TRACK
SPEECH
SIL
SPEECH
MUSIC
SPEECH
SIL
0:000:451:302:153:003:454:32
ACTION TRACK
INTRO
B-ROLL
ACTION
B-ROLL
ACTION
OUTRO
CONTENT CLASSIFICATION
PRODUCT DEMONSTRATION
INTERVIEW
TUTORIAL
SPEECH SEGMENTS
14 turns · 3m 12s
ACTION EVENTS
8 detected · 42s
QA STATUS
APPROVED ✓
Hours Annotated:4,200 hrs Action Classes:86 Languages:8 IAA κ:0.81
How It Works

Three things the pipeline does on every video annotation project

Temporal consistency enforcement
Bounding boxes, segmentation masks, and track IDs verified for frame-to-frame consistency. ByteTrack-assisted tracking flags identity switches and trajectory gaps automatically before human review.
ByteTrack-assisted multi-object tracking
AI pre-tracking identifies object trajectories; annotators verify, correct, and handle edge cases including occlusion, re-entry, and camera transitions. AI accelerates and humans verify.
Per-class temporal kappa scoring
Inter-rater agreement measured separately per object class and per temporal segment which is not averaged across the full video, which hides systematic annotation failures in specific scenario types.
Pipeline Capabilities

What the infrastructure delivers

Temporal Consistency Checking
Frame-level annotations are verified for object identity continuity across clip boundaries—ID switches, occlusion events, and re-entries all validated before delivery.
Sensor-Specific Taxonomies
Annotation ontologies are optimized per sensor type—RGB, thermal, depth, and radar each receive label hierarchies matched to what that sensor can and cannot resolve.
Scalable Edge-Case Coverage
Rare scenario detection pipelines route long-tail events to specialist review, ensuring edge cases are annotated before they become model blind spots at inference time.
Task Types

Five video annotation tasks, each temporally consistent

Object Tracking
Bounding box annotation with consistent track IDs across all frames. ByteTrack handles high-confidence tracking; human annotators handle occlusions, re-entries, and ID corrections. Supports multi-class multi-object tracking. Output: COCO tracking JSON or MOT Challenge format.
Action Recognition
Temporal labeling of actions performed by tracked entities walking, running, falling, fighting, cooking, assembling. Supports both clip-level classification and frame-level action boundaries (temporal action proposal annotation). Includes intensity and confidence scoring.
Temporal Segmentation
Dividing video into temporal segments by scene, activity, or content type. Scene boundary detection annotation, activity segmentation (shot type changes, action phase transitions), and narrative segmentation for surveillance and sports analytics applications.
AV Scenario Annotation
Autonomous vehicle-specific video annotation: object tracking of all road actors, lane detection across frames, traffic sign and signal recognition, drivable surface segmentation, and event annotation (near-misses, sudden stops, pedestrian encroachments).
Surveillance & Security
Person re-identification across camera views, anomaly detection event labeling, crowd density estimation annotation, and security incident classification. Privacy-preserving workflow with face blurring option. GDPR-aligned data handling throughout.
What You Get

Annotated video data backed by verifiable quality proof

Every video annotation project delivers three core outputs alongside the labeled dataset.

Annotated Video Dataset
Frame-by-frame or clip-level annotations in your format: CVAT XML, MOT Challenge, COCO Video, or JSONL. Includes bounding box tracks, segmentation sequences, action labels, or event timestamps with track IDs maintained across the full sequence.
QA Report with Temporal Kappa
Per-class and per-segment inter-annotator agreement, track identity consistency scores, frame coverage statistics, and a log of all adjudicated occlusion and re-identification decisions. Verifiable against raw annotation logs.
Data Card & Annotation Guide
ML data card documenting object classes, tracking methodology, edge case handling (occlusion, scene cuts, object entry/exit), quality thresholds, and annotation tooling. Full temporal labeling guidelines included.
Pricing

Per-minute video
pricing by complexity

Video annotation is priced per minute of footage based on scene complexity, object density, and task type. Temporal interpolation savings are passed directly to you you pay for human annotation time, not automated tracking.

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Simple tracking (low density, clear scenes)$10–18 / min
Complex tracking (dense, occlusions)$18–36 / min
AV scenario annotation$25–50 / min
Action recognition + tracking$30–60 / min
Minimum project30 minutes

Get 5 minutes of video annotated free

Send us a 5-minute clip from your dataset. We will return fully tracked and labeled video with IoU metrics and ID consistency report at no cost, no commitment.

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