AI-900 Practice Question: Describe features of computer vision workloads on Azure
What is 'object tracking' in computer vision and how does it differ from object detection?
⚠ Common exam trap
It's easy for candidates to confuse object detection (locating objects in a single frame) with object tracking (maintaining identity across frames), often selecting Option A because they think 'same object across images' implies tracking, but without temporal video context it is just detection or matching.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Maintaining the identity of detected objects across consecutive video frames with persistent IDs
Object tracking maintains the identity of detected objects across consecutive video frames by assigning persistent IDs, enabling the system to follow the same object over time. This differs from object detection, which identifies and locates objects in a single frame without preserving identity across frames. In Azure Video Indexer or Custom Vision, tracking is essential for scenarios like counting unique people or vehicles in a video stream.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Detecting the same object across multiple images in a photo album
Why it's wrong here
Photo album matching is an image retrieval task that compares visual features (e.g., embeddings from a CNN) to find similar instances across unordered still images. Unlike tracking, it does not rely on temporal continuity or a video frame sequence, so it cannot assign persistent identities that evolve with motion. Object tracking, by contrast, leverages temporal adjacency and motion prediction to maintain a consistent ID across consecutive frames.
- ✓
Maintaining the identity of detected objects across consecutive video frames with persistent IDs
Why this is correct
Object tracking assigns a unique identifier to each detected object at the first frame and then propagates that ID across subsequent frames by matching detections via spatial overlap, appearance similarity, or motion models (e.g., Kalman filters). This persistence of identity is what enables trajectory analysis, unique object counting, and behavior understanding over time. It is the core definition of visual object tracking.
- ✗
Monitoring GPS location of physical objects using IoT sensors
Why it's wrong here
GPS-based monitoring uses satellite triangulation and IoT telemetry to report geographic coordinates at intervals, which is completely independent of image or video analysis. Visual object tracking operates on camera image sequences, detecting objects via computer vision and linking them frame-to-frame based on pixel location and appearance. Therefore, GPS tracking is an unrelated sensor modality, not a form of computer vision tracking.
- ✗
Detecting when a tracked object leaves the camera's field of view
Why it's wrong here
Detecting when an object leaves the field of view is a specific tracking event that may trigger a handoff or count update, but it is only one application of tracking. The tracking algorithm itself must maintain the object's identity for every frame, including handling occlusions, re-entries, and trajectory estimation, not just flag an exit condition. Reducing tracking to exit detection ignores the broader continuous association process.
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Related to this question
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Azure Machine Learning Studio
Key term
Computer vision
Computer vision is a field of artificial intelligence that enables computers to interpret and make decisions based on visual data from the world, such as images and videos.
Key term
Indexer
An indexer is a software component that processes content and builds a searchable index so that users can quickly find documents, data, or files based on keywords or phrases.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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