Dwell Time Analysis
Dwell time analysis is an AI video analytics capability that measures how long individuals remain in a defined zone — a store aisle, a service desk, a platform, an intersection — and aggregates that data into actionable insights about attention, engagement, or congestion.
Dwell Time Analysis
Dwell time analysis is an AI video analytics capability that measures how long individuals remain in a defined zone — a store aisle, a service desk, a platform, an intersection — and aggregates that data into actionable insights about attention, engagement, or congestion.
How It Works
A dwell time pipeline combines detection, tracking, and zone mapping:
- Zone definition — operators mark the area of interest in each camera view.
- Person detection — every person in the frame is located.
- Tracking — a tracker assigns a persistent ID across frames as long as the person remains visible.
- Timing — the system measures how long each tracked ID stays inside the zone.
- Aggregation — average and distribution of dwell times are calculated per hour or day.
Why It Matters
Dwell time is one of the richest behavioral signals extractable from video:
- Retail engagement — longer dwell near a display signals interest; short dwell suggests the display isn't attracting attention.
- Service quality — dwell time at checkout approximates wait time.
- Congestion monitoring — sustained dwell in transit zones signals crowding.
- Attention measurement — dwell at digital signage indicates content effectiveness.
- Product displays — measuring attention to in-store features
- Service counters — estimating queue wait times
- Museums and venues — exhibit engagement metrics
- Transit platforms — crowding detection
- Public spaces — plaza and park usage patterns
IncoreSoft's Heat Map module tracks both spatial (where people go) and temporal (how long they stay) metrics, making it a foundational tool for retail analytics.
Use Cases
Frequently Asked Questions
How accurate is dwell time measurement?
Accurate to a few seconds when tracking is continuous. Errors occur when occlusions break the tracker's identity — but modern Re-ID-enhanced tracking minimizes this.
Does dwell time analysis identify individuals?
No — standard dwell time analytics are anonymous. Each tracked ID lives only for the duration of observation and is discarded afterward.
What can you learn from dwell time?
Where people linger (engagement), where they rush (discomfort or disinterest), and where they queue (service bottlenecks). Combined with conversion data (sales, transactions), dwell time helps optimize layouts, staffing, and content.
Read also
Fare Evasion Detection
Fare evasion detection is an AI video analytics capability that identifies when a passenger enters a paid-access zone — transit turnstile, platform gate, validation point — without paying the fare, and generates a real-time alert or evidence record for enforcement.
Cloud Video Analytics
Cloud video analytics is the deployment model where AI analytics run on centralized cloud servers — rather than on the camera (edge) or an on-premise server — with camera streams ingested over the internet and results delivered back to operators via web or mobile clients.
Camera Frame Rate
Camera frame rate — measured in frames per second (FPS) — is the number of images a camera captures and transmits each second. It is one of the most important trade-offs in any surveillance deployment, balancing smoothness, bandwidth, storage, and analytics accuracy.
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