Diving 3D Survey Analysis

• Diving 3D Survey is a comprehensive framework that defines dive events via probabilistic temporal localization using a sliding window TALNN and Hann smoothing.
• The system accurately tracks diver movements by utilizing segmentation-based hot-spot detection combined with RANSAC-fitted kinematic trajectory smoothing.
• Multi-task 3D convolutional classification deciphers dive codes—capturing rotations, poses, twists, and somersaults—with dilated filters enhancing temporal feature extraction.

A DIVING 3D Survey refers to a comprehensive methodology for extracting, tracking, and classifying diving actions from continuous video footage, with strong implications for high-precision motion analytics, sports performance evaluation, and the wider domain of video-based event analysis. The modular system described in (Nibali et al., 2017) addresses the challenging problem of isolating short-duration, fine-grained diver motions within long video streams, centering on robust event temporal localization, spatial localization (tracking), and nuanced classification of diver actions with high anatomical sensitivity.

1. Event Temporal Localization

A critical component of the DIVING 3D Survey framework is the accurate cropping of dive events from continuous video. This is achieved via a temporal action localization neural network (TALNN) that slides a 21-frame window across the timeline and computes three independent probability signals for each window center:

• $f_{start}(t)$: Probability of "start" (diver leaves platform)
• $f_{mid}(t)$: Probability of "mid" (airborne phase)
• $f_{end}(t)$: Probability of "end" (diver enters water)

Formally, these are given by:

$f_M(t) = \Pr(M \mid x_t)$

with $$M \in \{\mathrm{start}, \mathrm{mid}, \mathrm{end}\}$$ and $x_t$ the 21-frame input centered at $t$.

Due to high-frequency noise, the raw outputs $f_M(t)$ are smoothed using a Hann window:

$$g_M(t) = \frac{2}{T}\int_{t-T/2}^{t+T/2} f_M(\tau) \sin^2\left(\frac{\pi(\tau-t)}{T} + \frac{\pi}{2}\right)\,d\tau$$

where $T$ is the window size.

Peak detection in $g_{mid}(t)$ identifies candidate dive events, while the nearest peaks in $g_{start}(t)$ and $g_{end}(t)$ specify the event’s temporal boundaries. This multi-signal approach is robust against ambiguous transitions and contamination from bystanders.

2. Diver Spatial Localization and Trajectory Smoothing

Given the extremely small spatial footprint of the diver (occupying <1% of pixels per frame), spatial localization is performed via a segmentation-based network trained to produce a "hot-spot" at the diver’s location within small image patches. The segmentation branch uses a weighted binary cross-entropy loss:

$$\mathcal{L} = \frac{-y\log(\hat{y})}{2(1-\beta)} + \frac{-(1-y)\log(1-\hat{y})}{2\beta}$$

with $\beta > 0.5$ penalizing missed detections more than false positives. Blob detection post-processing yields centroids for each frame.

Global physics-based trajectory constraints are imposed using:

• Horizontal position: $x(t) = a_0 + a_1 t$ (linear in time)
• Vertical position: $y(x) = b_0 + b_1 x + b_2 x^2$ (quadratic in $x$)

Parameters are robustly fitted via RANSAC/MSAC to suppress noise and outliers:

Function CreateModel(ts[], xs[], ys[]) a₀, a₁ ← LinearRegression(ts, xs) b₀, b₁, b₂ ← QuadraticRegression(xs, ys) Model(t): x ← a₀ + a₁ t; y ← b₀ + b₁ x + b₂ x²; return (x, y)

This results in a kinematically plausible, temporally smooth diver trajectory even in cluttered scenes.

3. Multi-Task Dive Classification

After extracting and tracking, the system classifies each event into a composite dive code consisting of rotation type, pose type, number of somersaults, number of twists, and handstand start (binary).

The classifier is a multi-head 3D convolutional network (C3D backbone, with variants using batch normalization or dilated convolutions for increased temporal sensitivity). Each head predicts a distinct component of the code, advantaging from shared feature learning while maintaining attribute-specific focus.

• Input: Cropped, tracked clips, reduced to 16 frames per event.
• Outputs: Five parallel predictions for the action code.

Dilated convolutions increase the network’s temporal receptive field and allow for pose-discriminative feature extraction sensitive to nuances such as pike vs. tuck or twist count.

4. Temporal Boundary and Contamination Robustness

The start/mid/end TALNN approach dynamically adapts to event durations and suppresses contamination from bystanders or other divers, since their activation patterns will not correlate with the trained temporal structure of a canonical dive. This probabilistic event modeling, as opposed to fixed sliding windows, markedly improves segmentation between overlapping or adjacent events.

5. Pose Sensitivity and High-Resolution Cropping

Accurate event classification depends on the system’s capacity to resolve subtle differences in body articulation over many frames. Design choices underpinning this include:

• Superior spatial localization yielding tight crops centered on the diver.
• 3D convolutions handling spatiotemporal evolution of body pose.
• Use of dilated filters to preserve granularity of features through the network depth.
• Multi-task outputs ensuring discriminative representations across all dive code attributes.

6. Extensibility and Impact for 3D Survey/Analytics

The described DIVING 3D Survey methodology generalizes to other domains where actions of interest are brief, visually localized, and discriminatively encoded in subtle cues (e.g., gymnastics, tennis strokes). The three-stage pipeline—temporal cropping by probabilistic signals, segmentation-based tracking with kinematic smoothing, and hierarchical multi-label 3D classification—provides a reference architecture for high-fidelity sports analytics.

Moreover, deploying 3D convolutional architectures capable of capturing fine-grained motion over time is a necessary building block for future 3D survey systems that aim to analyze and index the articulatory and kinematic details of human performance using video data alone.

7. Summary Table: Key Components

Stage	Methodology	Output/Objective
Temporal Event Localization	21-frame window, TALNN, Hann smoothing	Start/mid/end probabilities, tightly cropped events
Spatial Diver Tracking	Segmentation hot-spot, RANSAC/kinematic model	Diver centroids, smoothed trajectory
Event Classification	Multi-head 3D C3D (with dilations)	Five-part action code predictions

This integrated workflow enables precise, resilient, and scalable monitoring of complex athletic actions and foreshadows developments in automated, high-resolution 3D motion analysis from video streams in challenging settings.