1. Who is primarily responsible for identifying moving objects from static scenes in video surveillance?
(A) Frame differencing
(B) Edge detection
(C) Background subtraction
(D) Histogram equalization
2. Which technique assumes that background remains nearly constant over time?
(A) Object tracking
(B) Thresholding
(C) Background subtraction
(D) Corner detection
3. Which of the following is a key application of background subtraction?
(A) Texture enhancement
(B) Optical character recognition
(C) Motion detection
(D) Image compression
4. What is typically subtracted in background subtraction techniques?
(A) Edges
(B) Foreground
(C) Background model from current frame
(D) Histogram values
5. Which method updates the background model over time to adapt to scene changes?
(A) Static background modeling
(B) Adaptive background modeling
(C) Morphological operations
(D) Region growing
6. What is the purpose of a learning rate in adaptive background subtraction?
(A) Increase resolution
(B) Control blending of new information
(C) Sharpen image edges
(D) Convert image to grayscale
7. Which statistical method is often used for adaptive background modeling?
(A) K-means clustering
(B) Gaussian Mixture Model
(C) Principal Component Analysis
(D) Linear Regression
8. Which problem does background subtraction aim to overcome in video analysis?
(A) Noise addition
(B) Motion blur
(C) Foreground segmentation
(D) Image stitching
9. Which of the following can affect the accuracy of background subtraction?
(A) Illumination changes
(B) High resolution
(C) Zoom level
(D) Histogram equalization
10. In a Gaussian Mixture Model, what does each Gaussian component represent?
(A) Edge gradient
(B) A possible background state
(C) A region of interest
(D) A foreground object
11. Which of the following is a challenge in background subtraction?
(A) Feature extraction
(B) Real-time implementation
(C) Interpolation
(D) Morphological filtering
12. Which metric is commonly used to compare the current frame with the background model?
(A) Euclidean distance
(B) Intensity difference
(C) Correlation coefficient
(D) Image entropy
13. What operation is commonly applied after subtraction to clean up the foreground mask?
(A) Histogram equalization
(B) Morphological operations
(C) Gaussian blurring
(D) Frequency filtering
14. Which condition can lead to ghost artifacts in background subtraction?
(A) Fast object motion
(B) Static lighting
(C) Stationary foreground objects
(D) Background smoothing
15. What is the initial step in any background subtraction method?
(A) Foreground classification
(B) Object tracking
(C) Background modeling
(D) Frame warping
16. Which background subtraction technique uses statistical learning?
(A) Frame differencing
(B) Optical flow
(C) Gaussian Mixture Model
(D) Laplacian filtering
17. Which condition is most ideal for simple background subtraction?
(A) Complex scenes
(B) Low light conditions
(C) Static camera with steady background
(D) Moving camera setup
18. What does a high learning rate in adaptive modeling result in?
(A) Better object contours
(B) Faster adaptation to changes
(C) Less noise
(D) Sharper background
19. Which technique can help reduce shadows in foreground masks?
(A) Sobel filter
(B) Color space transformation
(C) Histogram matching
(D) Median filtering
20. What role does thresholding play in background subtraction?
(A) Enhancing contrast
(B) Segmenting foreground from difference image
(C) Reducing brightness
(D) Performing zoom operations
21. What type of noise affects background subtraction in outdoor environments?
(A) Gaussian blur
(B) Salt and pepper noise
(C) Dynamic background noise
(D) JPEG artifacts
22. Why is frame differencing less robust than background modeling?
(A) Requires large memory
(B) Cannot handle static foreground
(C) Has slow processing speed
(D) Detects only corners
23. Which frame is used to initialize the background in a basic model?
(A) Midframe
(B) Average of first N frames
(C) First frame
(D) Last frame
24. Which component helps identify whether a pixel belongs to foreground or background?
(A) Pixel intensity
(B) Pixel variance
(C) Background model
(D) Color histogram
25. Which filter is often used to reduce noise before thresholding the foreground mask?
(A) Sobel filter
(B) Mean filter
(C) Laplacian filter
(D) High-pass filter
26. Which term describes objects that appear in the foreground mask but are not real motion?
(A) Occlusions
(B) Reflections
(C) False positives
(D) Motion blur
27. Which method performs subtraction between consecutive frames?
(A) Frame differencing
(B) Gaussian modeling
(C) Edge detection
(D) Background warping
28. What is the output of background subtraction?
(A) Motion vectors
(B) Optical flow
(C) Binary foreground mask
(D) Histogram plot
29. Which element is crucial for background subtraction in moving camera setups?
(A) Motion compensation
(B) Color matching
(C) Low-pass filtering
(D) Threshold adaptation
30. Which background subtraction approach is suitable for long-term outdoor surveillance?
(A) Frame differencing
(B) Static modeling
(C) Gaussian Mixture Models
(D) Bit plane slicing
31. Which property allows GMM to handle multimodal backgrounds?
(A) Linear regression
(B) Multiple Gaussian components per pixel
(C) One histogram per frame
(D) Adaptive thresholding
32. Which term defines the process of updating the background image?
(A) Masking
(B) Learning
(C) Registration
(D) Encoding
33. What kind of pixel changes are typically labeled as foreground?
(A) Slight fluctuations
(B) Static regions
(C) Significant deviations from background
(D) High-frequency details
34. What is a common drawback of using high thresholds in subtraction?
(A) Too many small blobs
(B) Missed moving objects
(C) Noise amplification
(D) Memory overflow
35. Which method is more suitable for real-time processing?
(A) Complex motion estimation
(B) Frame differencing
(C) Template matching
(D) Multi-camera analysis
36. What does a low learning rate cause in adaptive modeling?
(A) Fast updates
(B) Lag in adapting to scene changes
(C) Increased false alarms
(D) Increased frame rate
37. Which data structure can efficiently store background models in real-time systems?
(A) Array list
(B) Hash map
(C) Matrix
(D) Circular buffer
38. How can we suppress noise in the foreground mask?
(A) Contrast stretching
(B) Histogram equalization
(C) Morphological closing
(D) RGB to YUV conversion
39. What is typically used to evaluate the performance of a background subtraction algorithm?
(A) Compression ratio
(B) Signal-to-noise ratio
(C) Precision and recall
(D) Frame rate
40. Which color space transformation helps in handling illumination variations?
(A) RGB to HSV
(B) RGB to CMY
(C) RGB to BGR
(D) RGB to XYZ
41. Why is shadow detection important in background subtraction?
(A) Enhances object edges
(B) Reduces false positives
(C) Increases image sharpness
(D) Detects static objects
42. What is the impact of camera jitter on background subtraction?
(A) No effect
(B) Improved accuracy
(C) Misalignment and noise
(D) More compact foreground mask
43. Which method uses probability to classify pixels into foreground and background?
(A) Median filter
(B) Region growing
(C) Probabilistic background modeling
(D) Edge linking
44. Which algorithm is used for adaptive background modeling in OpenCV?
(A) Canny edge detector
(B) MOG (Mixture of Gaussians)
(C) Laplacian pyramid
(D) Mean-shift clustering
45. What does the acronym MOG stand for in background subtraction?
(A) Motion of Gaussians
(B) Mixture of Gaussians
(C) Mode of Gradients
(D) Mask of Graphics
46. Which foreground detection issue arises from objects with similar color as the background?
(A) Illumination invariance
(B) Color camouflage
(C) Foreground blending
(D) Contrast suppression
47. What is the ideal condition for foreground extraction in video?
(A) Fast camera motion
(B) Low light
(C) Static background
(D) Varying focus
48. What is the foreground mask composed of?
(A) RGB pixels
(B) Binary values
(C) Gaussian weights
(D) Histogram bins
49. Which of the following is not typically a part of background subtraction?
(A) Background modeling
(B) Subtraction
(C) Histogram equalization
(D) Foreground detection
50. What challenge does weather introduce to background subtraction?
(A) Reduced contrast
(B) Increased pixel similarity
(C) Dynamic background noise
(D) Smooth motion
