Computer Vision Interview
20 essential Q&A
Updated 2026
Kalman
Kalman Filter for Tracking: 20 Essential Q&A
Linear Gaussian state estimation—predict with motion model, correct with measurements.
~11 min read
20 questions
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predictupdateQ/REKF
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1. What is a Kalman filter?
2. Linear Gaussian model
3. State vector (bbox)
4. Predict step
5. Update / correct
6. Kalman gain
7. Process noise Q
2. Linear Gaussian model
3. State vector (bbox)
4. Predict step
5. Update / correct
6. Kalman gain
7. Process noise Q
1
What is the Kalman filter?
📊 medium
Answer: Optimal recursive estimator for linear systems with Gaussian noise—alternates prediction from dynamics and correction from noisy observations.
2
State-space form?
🔥 hard
Answer: x_{k+1} = F x_k + w_k (process noise), z_k = H x_k + v_k (measurement noise)—Kalman assumes linear F,H and Gaussian w,v.
3
Typical bbox state in SORT?
📊 medium
Answer: Often [cx, cy, s, r, vx, vy, vs] (center, scale area-ish, aspect, velocities)—measurements update subset.
4
Predict step?
📊 medium
Answer: x̂− = F x̂, P− = F P Fᵀ + Q—propagate mean and covariance forward in time without new measurement.
5
Update step?
📊 medium
Answer: Fuse measurement z using Kalman gain K: x̂ = x̂− + K(z − H x̂−), P = (I − K H) P−—reduce uncertainty along observed dimensions.
6
Kalman gain meaning?
🔥 hard
Answer: K balances trust in prediction vs measurement based on covariances—if R small (accurate sensor), K larger, trust measurement more.
7
Tune Q?
📊 medium
Answer: Process noise covariance—higher Q = more model uncertainty, tracker follows measurements faster but noisier.
8
Tune R?
📊 medium
Answer: Measurement noise—higher R = smoother track, lag on maneuvers; lower R = jittery if detector noisy.
9
Constant velocity model?
⚡ easy
Answer: Assumes derivative of position constant between frames—simple, works for smooth motion; fails on sharp turns.
10
Constant acceleration?
📊 medium
Answer: Adds acceleration state for more expressive motion—better for maneuvering targets, more parameters to tune.
11
When EKF?
🔥 hard
Answer: Nonlinear dynamics or measurement—linearize with Jacobians around current estimate; no longer globally optimal but widely used.
12
UKF / particle?
🔥 hard
Answer: Handle stronger nonlinearities—UKF uses sigma points; particle filters for non-Gaussian multimodal posteriors (rare in simple MOT).
13
Missing detection?
⚡ easy
Answer: Skip update; covariance grows with prediction-only steps until next match—standard in SORT when object temporarily not detected.
14
Multi-dimensional measurements?
📊 medium
Answer: H maps state to observed variables (e.g. only position observed, not velocity directly inferred from motion over time).
15
What is P?
📊 medium
Answer: State estimate covariance—uncertainty ellipsoid; should shrink after informative updates.
16
Initialize velocity?
⚡ easy
Answer: From finite differences of first two boxes or zero velocity with high initial P—tradeoff between fast lock vs overshoot.
17
SORT’s use?
📊 medium
Answer: Each track maintains Kalman state; Hungarian matches detections to predicted boxes—simple, fast MOT baseline.
18
OpenCV?
⚡ easy
Answer:
cv2.KalmanFilter with transition/measurement matrices—set dt, Q, R for bbox tracking experiments.
import cv2
kf = cv2.KalmanFilter(4, 2) # example dims
19
Numerical issues?
🔥 hard
Answer: Use Joseph form for P update, symmetric enforcement, or square-root filtering if covariance becomes indefinite.
20
When Kalman fails?
📊 medium
Answer: Highly nonlinear motion, multi-modal uncertainty (occlusions), or heavy-tailed detector noise—consider particle, IMM, or learning-based motion.
Kalman Cheat Sheet
Loop
- Predict
- Update
Tuning
- Q process
- R measure
Nonlinear
- EKF
💡 Pro tip: K trades prediction uncertainty vs measurement noise.
Full tutorial track
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