quiz.json

{
  "questions": [
    {
      "stage": "pre",
      "question": "What does the Hungarian algorithm do in tracking-by-detection?",
      "options": ["It filters out low-confidence detections", "It solves the minimum-cost one-to-one assignment between tracks and detections — typically using 1 - IoU as cost — to match each track to at most one detection and vice versa", "It trains the detector", "It predicts future trajectories"],
      "correct": 1,
      "explanation": "Every tracking-by-detection frame does an assignment problem: M tracks, N detections, find the best one-to-one match. Hungarian is the optimal polynomial algorithm for this. Cost is usually 1 - IoU (higher overlap = lower cost). scipy.optimize.linear_sum_assignment is the standard implementation. SORT, DeepSORT, ByteTrack, BoT-SORT all use it."
    },
    {
      "stage": "pre",
      "question": "ByteTrack's distinguishing contribution is what?",
      "options": ["A new backbone", "A second-stage association that tries to match leftover tracks to low-confidence detections, recovering short occlusions and IDs that standard trackers would lose by dropping those detections", "A new Kalman filter", "An appearance embedding"],
      "correct": 1,
      "explanation": "Most trackers discard detections below a confidence threshold (~0.5). ByteTrack keeps them and runs a second Hungarian pass to try to match them to tracks that did not match any high-confidence detection. This recovers brief occlusions and crossings, lifting IDF1 significantly on MOT17 without any learned appearance features. Its simplicity makes it the default in Ultralytics and Roboflow Supervision."
    },
    {
      "stage": "post",
      "question": "How does SAM 2's memory-based tracker avoid explicit Hungarian-style association?",
      "options": ["It runs SORT internally", "It stores per-instance spatio-temporal features in a memory bank; on each new frame, cross-attention between memory and current features directly produces the mask of the same instance, with association implicit in the attention operation", "It uses a Kalman filter", "It requires hand-labelled IDs per frame"],
      "correct": 1,
      "explanation": "SAM 2 replaces the detect-then-associate loop with a memory-conditional segmenter. The memory bank holds the instance's features from previous frames. At each new frame, the decoder cross-attends the new frame's features against the memory, and the resulting mask is the same instance — no external assignment step. This handles long occlusions gracefully because the memory stays even when the instance disappears for many frames."
    },
    {
      "stage": "post",
      "question": "For surveillance video where keeping each person's ID consistent is the primary requirement, which metric should you report?",
      "options": ["MOTA", "IDF1 — the harmonic mean of ID precision and recall; measures identity preservation over time rather than per-frame detection accuracy", "Top-1 accuracy", "FID"],
      "correct": 1,
      "explanation": "MOTA conflates detection and association errors, so a tracker with many false positives can hide its ID failures. IDF1 is designed for the 'who is who over time' question: it matches predicted tracks to ground-truth tracks globally across the whole video and computes F1 on the identity labels. For surveillance and crowd analytics, IDF1 is the metric to report; HOTA is the broader academic standard."
    },
    {
      "stage": "post",
      "question": "SAM 3.1 Object Multiplex (March 2026) introduces shared memory across many tracked instances. What does that enable?",
      "options": ["Lower accuracy", "Efficient multi-object tracking — one shared memory bank with per-instance query tokens replaces N separate memory banks, so cost scales sub-linearly in number of instances and concert-crowd-sized scenes become tractable", "Cloud-only inference", "A new training objective"],
      "correct": 1,
      "explanation": "Pre-Multiplex SAM 2 / SAM 3 tracked each object with its own memory bank; cost scaled linearly with the number of instances. Multiplex (March 2026) introduces one shared memory with per-instance query tokens that fetch instance-specific features. Many-object tracking — crowds, traffic, warehouse workers — becomes efficient for the first time with a memory-based tracker."
    }
  ]
}