CCTV Video Analytics and Advanced Features Flashcards
6 cards from real CCTV practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 CCTV Video Analytics and Advanced Features flashcards as text
What does 'people counting' analytics in CCTV measure?
Answer: The number of individuals entering or exiting a defined area or crossing a line
People counting analytics uses video processing to accurately count individuals entering or exiting a space, useful for occupancy management and retail analytics.
In video analytics, what is 'object classification'?
Answer: The ability to distinguish between different types of detected objects such as person, vehicle, or animal
Object classification uses machine learning to identify and label detected objects as specific categories (person, vehicle, bicycle, etc.) to reduce false alarms from non-relevant motion.
What is a 'heat map' generated by CCTV analytics used for?
Answer: Visualizing areas of high activity or movement frequency over time
Analytics heat maps use color overlays on the camera image to show which areas of the scene have experienced the most activity over a defined time period.
What advantage does edge-based video analytics (processing on the camera) offer compared to server-based analytics?
Answer: Reduced bandwidth and server load since processing occurs locally at the camera
Edge analytics performs video processing inside the camera itself, sending only metadata or alerts to the server, which significantly reduces network bandwidth usage.
What is 'facial recognition' in a CCTV analytics system designed to do?
Answer: Match captured faces against a database to identify or flag specific individuals
Facial recognition analytics extracts biometric facial features from video frames and compares them against an enrolled database to identify or flag persons of interest.
Which of the following is a common cause of false alarms in motion-triggered CCTV analytics?
Answer: Environmental motion such as tree branches, shadows, or lighting changes
Environmental factors like moving vegetation, cloud shadows, headlight sweeps, and rapid lighting changes can trigger motion detection algorithms inappropriately.