What Is Object Detection And Tracking in Video Analytics?

What Is Object Detection And Tracking?

Object detection and tracking are the core technical capabilities that make video analytics useful for security. Object detection answers “what is in the frame” and “where is it.” Object tracking answers “is it the same entity over time” and “what is it doing.” When these are engineered well, a CCTV system can produce reliable events such as perimeter breaches, loitering, and unauthorised vehicle movement while minimising false alarms from weather, wildlife, and shadows.

Read on to learn more about how object detection and tracking works and why it’s one of the best CCTV analytics solutions.

TLDR

Object detection uses deep learning to identify people, vehicles, and other classes in video frames. Tracking links those detections over time to create motion paths, dwell time, and direction data. Together they enable accurate intrusion detection, behaviour analysis, forensic search, and PTZ auto tracking.

What You Will Learn

• how object detection models work

• how tracking maintains identity

• what metrics determine quality

• how these capabilities are applied

Object Detection Fundamentals

Now, let’s dive into the basics covering what object detection is and how it works.

How modern detectors work

Detectors ingest an image tensor and output bounding boxes plus class probabilities.

In security, classes usually include person and vehicle as baseline, with optional classes such as animal, bicycle, face, or licence plate region depending on vendor.

Axis describes its object analytics as detecting, classifying, tracking, and counting objects.

Pixel density and detection limits

Detection quality depends on pixels per metre on target. If a person at the fence line occupies too few pixels, classification becomes unstable.

This is why lens choice and camera placement are foundational.

For outdoor perimeters, you often prioritise a narrower field of view with higher pixel density over a wide view that cannot classify reliably.

Object Tracking And Identity Over Time

Data association

Tracking links detections across frames by estimating motion and comparing appearance features.

Challenges include occlusion, sudden lighting changes, and crowded scenes.

The tracker must decide whether a new detection is the same object or a new one.

Re identification and cross camera tracking

Some platforms support re identification, matching the same person or vehicle across cameras using embeddings.

This is valuable for investigations and multi camera perimeter corridors.

Use Cases for Object Identification and Tracking Analytics

Intrusion and perimeter breach detection

Tracking turns a single detection into a validated approach and crossing.

A person moving toward a fence line, pausing, then crossing a line is a higher confidence intrusion event than a single frame motion trigger.

Object left and object removed

These events rely on background modelling plus classification.

The system detects a stationary object appearing or disappearing in a zone, then confirms it matches a relevant class.

PTZ auto tracking

In architectures where a fixed camera detects and tracks, it can hand off to a PTZ to maintain visual lock.

FLIR describes thermal systems that can hand off classified intrusions to PTZ cameras for autonomous tracking.

Quick Summary

Object detection and tracking provide the machine perception layer for modern CCTV. When combined with strong camera design and rule tuning, they enable reliable threat detection, lower false alarms, and faster verification across perimeter and site security.

Related Reading: The Different Types of Video Analytics Software