서브프레임 정밀도
선수가 프레임 캡처의 정확한 순간에 게이트를 통과하는 경우는 드뭅니다. 우리 시스템은 여러 프레임을 분석하여 프레임 사이의 통과 순간을 정확히 파악합니다 — 카메라의 기본 프레임 간격을 훨씬 뛰어넘는 서브밀리초 보간 정확도를 달성합니다.
The interpolation works by observing the athlete's torso position in at least 3 consecutive frames as they approach and cross the timing line. By fitting a linear motion model to these positions, we solve for the precise timestamp when the torso center crossed the gate position. Because human sprinting velocity is nearly constant over short distances (the 1-2 meter detection zone), the linear model produces highly accurate results.
물리 기반 보정
phone 카메라는 각 프레임을 위에서 아래로 수 밀리초에 걸쳐 스캔하며, 선수가 프레임 내 어디에 나타나느냐에 따라 타이밍 오프셋이 발생합니다. 이러한 오프셋과 기타 하드웨어 수준의 타이밍 아티팩트를 자동으로 보정하여 체계적 오차를 제거합니다.
TrackSpeed automatically measures the rolling shutter duration for each device and applies a per-pixel timing correction. If an athlete's torso is detected at 40% of the frame height and the rolling shutter scan takes 10ms, we apply a 4ms correction to the timestamp. This correction is applied before interpolation, ensuring the trajectory regression operates on accurate timing data. The result: rolling shutter becomes a non-factor in the final timing.
다중 기기 시계 동기화
출발점에 한 대, 결승점에 한 대 — 두 대의 phone을 사용할 때 시계는 밀리초 이내로 일치해야 합니다. 우리의 동기화 프로토콜은 기기 간 ±3-5ms 정렬을 달성합니다. 동기화 중에는 두 폰을 약 10m 이내에 두세요. 동기화가 완료되면 필요한 만큼 멀리 이동하세요.
The sync protocol sends multiple round-trip timing probes between the two phones via peer-to-peer WiFi. By measuring send and receive timestamps on both devices, we calculate the clock offset using the same mathematical principles as the Network Time Protocol (NTP). Multiple samples are taken and outliers are rejected, achieving ±3-5ms alignment between devices. During sync, keep both phones within about 10 meters of each other for best results. Once synced, move them as far apart as needed — the offset stays locked.
빔 차단이 아닌 신체 추적
레이저 게이트는 빔을 가장 먼저 차단하는 것에 반응합니다 — 뻗은 팔, 무릎 또는 가슴. 이는 달리기 자세에 따라 달라져 일관성이 떨어집니다. TrackSpeed는 선수의 신체 질량을 추적하여 상체가 라인을 통과한 시점을 판단하며, 이는 공식 타이밍 방식과 일치합니다.
TrackSpeed's detection engine replicates the official approach. Our geometry-based algorithm processes each video frame at 180×320 resolution, using frame differencing to detect motion, connected component labeling to identify body regions, and size/shape filters to isolate the torso from limbs. An arm-spike suppression algorithm prevents raised arms from triggering false crossings. The result: consistent chest-based timing that matches the official standard, unlike laser gates that trigger on the nearest body part.
서브프레임 보간, 물리 보정, 시계 동기화의 조합으로 전용 하드웨어에 필적하는 타이밍 정밀도를 제공합니다 — phone만으로.
다른 시스템과의 비교
| 시스템 | 해상도 | 감지 방식 | 비용 |
|---|---|---|---|
| FAT (공식) | 0.001s | 사진 판정 카메라 | $10,000+ |
| 레이저 게이트 | ±50-200ms* | 첫 빔 차단 | $500-2000 |
| TrackSpeed | ~4ms | 신체 추적 | phone |
The Detection Pipeline
Every video frame captured by your phone's camera goes through a multi-stage detection pipeline. Here's what happens in the roughly 2ms it takes to process each frame:
1. Downsample & Extract Luma
The raw camera frame is downsampled to 180×320 pixels and converted to a grayscale (luma) image. This resolution is sufficient for accurate body detection while keeping processing fast enough for real-time analysis at 120fps.
2. Frame Differencing
Each frame is compared against the previous frame (N-1 differencing). Pixels that changed significantly — above a fixed threshold of 15 — indicate motion. This produces a binary motion mask showing where the athlete is moving.
3. Connected Component Labeling
The motion mask is analyzed using an 8-connected component labeling algorithm, which groups adjacent motion pixels into distinct blobs. Each blob is a candidate body region.
4. Size & Shape Filters
Candidate blobs are filtered by physical plausibility. A valid body region must be at least 33% of the frame height and 8% of the frame width. Fill ratio (minimum 0.20) and aspect ratio (maximum 1.2) filters reject noise, shadows, and camera shake artifacts.
5. Gate Crossing Detection
When a validated body region crosses the timing line position, a crossing event is triggered. A 0.5-second cooldown prevents double-counting from the same athlete, and gate-occupied tracking ensures each crossing is recorded exactly once.
6. Position-Based Interpolation
The crossing timestamp is refined using trajectory regression across the surrounding frames. This is where the sub-frame precision comes from — converting a discrete frame event into a continuous-time measurement with ~4ms accuracy.
Device Stability & Thermal Management
Accurate detection requires a stable camera. TrackSpeed monitors your phone's gyroscope in real-time, alerting you if the device moves during timing. The IMU (Inertial Measurement Unit) detects rotation rates as small as 0.15 rad/s — about the level of vibration from a passing truck.
Running the camera at 120fps is computationally intensive. TrackSpeed continuously monitors the device's thermal state and will warn you if the phone is overheating, which can cause frame drops and reduce timing accuracy. For sessions longer than 30 minutes, we recommend using 60fps, which maintains ~6ms accuracy with significantly lower thermal impact.
Frequently Asked Questions
How accurate is phone-based sprint timing compared to laser gates?
TrackSpeed achieves ~4ms effective timing accuracy, which is more consistent than laser gates for training purposes. Laser gates have sub-millisecond trigger speed, but they trigger on whichever body part breaks the beam first — hand, knee, or chest — creating 50-200ms of run-to-run variability. TrackSpeed tracks the chest crossing, matching official timing standards and eliminating this inconsistency.
Does TrackSpeed work without internet or WiFi?
Yes. Single-phone timing works completely offline. Multi-phone timing uses peer-to-peer WiFi Direct between the devices — no internet connection or router needed. The phones connect directly to each other.
Can TrackSpeed replace professional timing equipment?
For training purposes, yes. TrackSpeed provides the consistency and accuracy needed to track progress, compare training sessions, and make coaching decisions. For official competition results that go on record, Fully Automatic Timing (FAT) systems with photo finish cameras remain the standard — they achieve 0.001s resolution. TrackSpeed is designed for the 99% of timing that happens in training, not the 1% that happens at meets.
Why does frame rate matter for timing accuracy?
Higher frame rates give more data points for trajectory regression. At 30fps, frames are 33.3ms apart; at 60fps, 16.7ms; at 120fps, 8.3ms. More frames near the crossing moment means better interpolation accuracy. However, even at 30fps, sub-frame interpolation achieves ~10ms accuracy — still far better than manual stopwatch timing (~200ms human reaction time error).
How does multi-phone timing work?
Place one phone at the start line and one at the finish. The phones connect via peer-to-peer WiFi and synchronize their clocks using an NTP-style protocol (±3-5ms accuracy). Each phone independently detects when the athlete crosses its line. The finish phone subtracts the start timestamp from the finish timestamp to calculate the elapsed time, corrected for the measured clock offset.
What happens if my phone overheats during a session?
TrackSpeed monitors thermal state and warns you before accuracy degrades. At 120fps, sustained recording can cause thermal throttling after 20-30 minutes, leading to dropped frames. If this happens, the app suggests switching to 60fps, which uses significantly less power while maintaining ~6ms timing accuracy. For long practice sessions, 60fps is recommended from the start.
훈련 타이밍을 시작할 준비가 되셨나요?
TrackSpeed를 다운로드하고 phone만으로 밀리초 정밀도를 경험하세요.
