Surveillance and Security Data Annotation for Threat Detection Tracking AI

Crowd models must read busy scenes where people overlap and move in different directions. Our video annotation services label individuals, groups, and density levels in stadiums, stations, malls, and public spaces, using bounding boxes, point markers, and density tags. Annotators follow your counting and occlusion rules, so models learn to estimate crowd size and movement reliably.

Threat detection models need clear examples of weapons in varied conditions. We annotate firearms, knives, blunt objects, and other items you define, using bounding box annotation and polygons on images and video. Annotators label objects that are partly hidden, held at different angles, or captured at long range, so models are trained on realistic scenes and not only on clean samples.

Face detection locates every face in an image, and recognition models learn to tell them apart. We place bounding boxes around faces and add landmark annotation and key point annotation on features such as the eyes, nose, mouth, and jawline. Consistent landmark schemas help models handle different angles, lighting, expressions, and partial coverage.

Person detection models need to find individuals reliably in crowded and cluttered scenes. We draw bounding boxes around each person and follow them across frames with persistent IDs, correcting identity when people overlap, leave the view or return. This human-annotated data trains models that count people, follow movement, and support video analytics in computer vision.

Vehicle models must recognize cars, trucks, buses, two-wheelers, and other road users across fixed and moving cameras. Annotators label each vehicle by class, track it from entry to exit, and maintain its identity through occlusion. These labels support traffic monitoring, perimeter control, and incident review.

Suspicious activity models learn from footage where unusual behavior is clearly tagged. We annotate actions and sequences that you define as suspicious, such as unusual movement, interaction with restricted objects, or deviations from normal patterns, and label each event with start and end times. Clear definitions and review checks keep tags consistent, so models learn from dependable examples.

Theft and intrusion models rely on labeled footage of forced entry, unauthorized access, and removal of property. We annotate people, objects, and actions across the sequence, tagging each stage of the event. This helps models recognize the behavior early enough for security teams to respond.

Prohibited item models must recognize restricted objects in bags, on people, and in scenes. We label items from your restricted list using bounding boxes and polygons, including items that are partly obscured or packed among others. Annotators follow clear class definitions, which reduces missed detections and false alarms at screening points.

Checkpoint footage and scans need careful labeling of people, belongings, and screening equipment. We annotate individuals, bags, items on conveyors and screening stations, and tag events such as bag placement, removal, and secondary checks. These labels support models that speed up screening while keeping procedures consistent.

Night vision images are low in color and contrast, which makes labeling difficult. Our image annotation services cover people, vehicles, animals, and objects in low-light and near-infrared captures, with annotators trained on how subjects appear in these conditions. Accurate labels help models perform when daylight cameras cannot.

Thermal images show heat signatures, so objects look different from visible-light photos. We annotate people, vehicles, animals, and equipment in thermal and infrared footage, and tag heat-related features where your project needs them. This supports detection in darkness, smoke, fog, and glare.

Drone footage presents small objects, changing altitude, and moving cameras. We annotate people, vehicles, structures, and activities in aerial images and video, and track them across frames with consistent IDs. Annotators handle varied scale and angle, so models perform well in border, perimeter, and large-area monitoring.

Access control models need to know who and what enters controlled areas. We annotate people, badges, doors, gates, vehicles, and entry events, and tag authorized and unauthorized access according to your rules. These labels help models support entry logging, tailgating detection, and real-time monitoring.

Abandoned object models must distinguish unattended items from those a person is still using. We label bags, packages, and objects, link them to nearby people, and tag when they are left behind. Time-based labeling gives models the context they need to raise alerts in stations, airports, and public venues.

Plate detection locates the plate on each vehicle, and recognition reads its characters. We draw bounding boxes around plates and, where your project requires it, transcribe the characters. Annotators work with angled, blurred, dirty, and partly covered plates, following defined guidelines for each case.

Action recognition models learn what people are doing, not just where they are. We tag activities such as walking, running, falling, fighting, carrying objects, and entering or leaving areas, with start and end times on video. Consistent action definitions help models separate similar movements and reduce confusion between classes.

Loitering models flag people who stay in one place too long, and restricted area models flag entry into zones that are off-limits. We label people, defined zones, and dwell durations, and tag each breach with its time and duration. Clear zone boundaries and timing rules keep labels precise and repeatable.

Hazard models must recognize danger early, often when the signs are small. We annotate flames, smoke, sparks, spills, gas clouds, and other hazards in images and video using polygons and segmentation masks. Careful labeling of faint and spreading smoke helps models detect incidents before they escalate.

Compliance models check whether people are protected for the task and area. We label helmets, vests, gloves, glasses, masks, and harnesses, and tag each person as compliant or non-compliant under your site rules. Consistent labeling across distances and angles helps models flag real breaches and limit false alerts.

Security cameras produce continuous footage across many views and conditions. Our video annotation services label people, vehicles, objects, and events across fixed and multi-camera setups, with timestamps and persistent IDs. The output is human-annotated data that models can use for detection, tracking, and incident review.

Object detection annotation finds each item of interest in a frame, and multi-object tracking follows every item across time. We label classes you define, keep identities consistent through occlusion and crossings, and review tracks for drift. This combination supports surveillance models that count, follow, and analyze many objects at once.

Scene and terrain models need to understand the environment around the camera or sensor. We apply semantic segmentation services to separate ground, roads, buildings, vegetation, water, sky, and obstacles, and label terrain types according to your schema. These pixel-level labels support navigation, route planning, and situational awareness.

Defense models need to identify vehicles and equipment from ground, aerial, and satellite views. We annotate vehicle types, weapons platforms, support equipment, and related infrastructure using bounding boxes and polygons, following class lists supplied by your team. Work is carried out under strict confidentiality in secure, access-controlled delivery environments.

Aircraft and vessel models must recognize craft at varied scales, distances, and angles. We label aircraft, ships, boats, and related features in aerial, satellite, and camera imagery, classifying each by type according to your taxonomy. Consistent labels help models support airspace, port, and maritime monitoring.

Satellite imagery covers vast areas where objects appear small. We annotate buildings, roads, vehicles, aircraft, vessels, land cover, and other features using polygons, boxes, and segmentation masks. Annotators follow your resolution and class rules, so geospatial models learn consistent patterns across scenes.

Moderation models need clear labels for content that breaks policy. We review images, video, and text and classify material such as violence, explicit content, hate symbols, and prohibited items according to your guidelines. Annotators work to defined escalation and review steps, so decisions remain consistent and auditable.