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TransNet V2: An effective deep network architecture for fast shot transition detection

Tomáš Souček, Jakub Lokoč

arXiv:2008.04838v1cs.CV

TL;DR

Shot-transition detection remains vulnerable to diverse open-world video content, including abrupt changes, difficult long transitions, and novel transition types. The paper presents TransNet V2, an improved deep architecture trained with synthetic transitions and enhanced temporal and similarity features, and reports state-of-the-art performance across three benchmarks. A pretrained open-source instance is provided for efficient large-scale video analysis.

  • Problem

    Open-world video diversity still produces false hits and missed transitions, so shot-transition detection is not considered solved.

  • Method

    TransNet V2 combines an improved dilated convolutional architecture, learned frame similarities, dual prediction heads, and synthetic transition training.

  • Results

    TransNet V2 represents a state-of-the-art approach on the ClipShots, BBC, and RAI benchmark datasets.

  • Takeaways & Limitations

    The provided pretrained open-source model can be integrated into video preprocessing pipelines requiring shot information.

Abstract

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Although automatic shot transition detection approaches are already investigated for more than two decades, an effective universal human-level model was not proposed yet. Even for common shot transitions like hard cuts or simple gradual changes, the potential diversity of analyzed video contents may still lead to both false hits and false dismissals. Recently, deep learning-based approaches significantly improved the accuracy of shot transition detection using 3D convolutional architectures and artificially created training data. Nevertheless, one hundred percent accuracy is still an unreachable ideal. In this paper, we share the current version of our deep network TransNet V2 that reaches state-of-the-art performance on respected benchmarks. A trained instance of the model is provided so it can be instantly utilized by the community for a highly efficient analysis of large video archives. Furthermore, the network architecture, as well as our experience with the training process, are detailed, including simple code snippets for convenient usage of the proposed model and visualization of results.

1 INTRODUCTION

Shot transition detection remains difficult in open-world video despite long-standing research and strong benchmark performance. TransNet V2 addresses this gap with an improved deep architecture, accessible implementation, and evaluation across three benchmarks.

  • 1 INTRODUCTION: Open-world video content still causes false hits, missed difficult transitions, and failures on novel transition types.Fast abrupt visual changes and long transitions can confuse trained models.
  • 1 INTRODUCTION: Temporal context is essential for transition detection, motivating frame-feature aggregation or 3D convolutions that jointly process spatial and temporal information.Prior approaches used both feature aggregation and 3D convolutional networks.
  • 1 INTRODUCTION: Earlier 3D-convolution approaches differed in post-processing, model size, and performance, while TransNet combined end-to-end prediction without post-processing with competitive accuracy.The comparison is described on the RAI dataset.
  • 1 INTRODUCTION: TransNet V2 improves the TransNet architecture and provides an open-source trained instance with training and evaluation code.The implementation is intended for convenient use and efficient processing of larger datasets.
  • 1 INTRODUCTION: Across ClipShots, BBC, and RAI, the authors report that TransNet V2 represents a state-of-the-art approach.Related models were re-evaluated with a unified evaluation script where information was available.

2 TRANSNET V2

The paper presents TransNet V2 as a deep shot-transition detector and documents its architecture, training experience, evaluations, and practical usage. It also supplies examples for integrating a pretrained model into video-analysis workflows.

  • 2 TRANSNET V2: TransNet V2 is presented as an enhanced model for shot detection.The section introduces the model and summarizes its development.
  • 2 TRANSNET V2: The paper details architectural changes and the training process for TransNet V2.It also re-evaluates selected related architectures.
  • 2 TRANSNET V2: The section concludes with evaluations of TransNet V2 and selected related work architectures.More comprehensive architectural and evaluation details are deferred to a forthcoming thesis.

2.1 Architecture

TransNet V2 extends dilated temporal processing with normalized, factorized, and skip-connected convolutional components, while adding frame-similarity features and dual prediction heads.

  • 2.1 Architecture: Every second DDCNN cell adds a skip connection and spatial average pooling to reduce spatial dimensions.These changes are included in the overall TransNet V2 architecture.
  • 2.1 Architecture: Factorized convolutions separate spatial image-feature extraction from temporal feature comparison and can reduce learnable parameters.The reduction depends on using a sufficiently low number of spatial convolution filters.
  • 2.1 Architecture: The model combines RGB histograms and learned features with cosine similarities to 50 preceding and following frames.Similarity vectors are transformed by a dense layer and concatenated with other inferred features.
  • 2.1 Architecture: Two prediction heads respectively identify a single middle transition frame and all transition frames.The all-frame head updates training weights rather than directly determining the final prediction.

2.2 Training Setup and Experience

TransNet V2 is trained mainly with synthetically rendered transitions assembled from reference shots, complemented by augmentation and a defined optimization procedure. The setup emphasizes varied transition lengths and gradual dissolves.

  • 2.2.1 Train datasets: Synthetic transitions are rendered on the fly from randomly selected shots in the TRECVID IACC.3 reference collection.The authors also experimented with real transitions from ClipShots.
  • 2.2.1 Train datasets: Training sequences contain 100 frames, with synthetic examples formed by joining randomly cropped segments from two reference scenes.Segments are sampled from scene start, center, and end regions when sufficiently long.
  • 2.2.1 Train datasets: Synthetic transitions vary in position, while generated hard cuts and dissolves span 2 to 30 frames.Sequences without transitions are excluded because hard negatives are assumed to occur in used input sequences.
  • 2.2.1 Train datasets: All frames in a shot receive the same geometric and color augmentation to avoid creating artificial shot boundaries.The augmentations include flips and TensorFlow operations on saturation, contrast, brightness, and hue.
  • 2.2.1 Train datasets: The network is optimized with weighted cross-entropy, L2 regularization, and SGD with momentum 0.9 for 50 epochs.Training uses a fixed learning rate of 0.01 and 600,000 transitions in total.

2.3 Comparison To Related Work

The paper re-evaluates related shot-transition models under a shared protocol and compares them with TransNet V2 across three benchmark datasets. TransNet V2 is preferable on ClipShots and BBC Planet Earth and comparable on RAI.

  • Re-evaluation Protocol for Related Models: 93.4% is the reported F1 score for DeepSBD on the RAI dataset, while DSM reports 93.5%.These two models were selected as state-of-the-art baselines primarily according to their reported RAI F1 scores.
  • Re-evaluation Protocol for Related Models: The re-evaluation uses publicly available network outputs when the original post-processing or multi-step implementations are unavailable.DeepSBD is evaluated using softmax predictions, while DSM is represented by its publicly available ResNet-18 baseline.
  • Shot detection and results: TransNet V2 predicts confidence for N = 100 input frames but evaluates only the middle 50, using a fixed threshold of 0.5 from the single-frame head.The remaining predictions are excluded because they have limited temporal context, and the second head is not used for transition prediction.
  • Re-evaluation Protocol for Related Models: TransNet V2 is compared with related works using F1 scores on ClipShots, BBC Planet Earth, and RAI.The comparison covers the official ClipShots test set, the BBC Planet Earth documentary series, and the RAI dataset.
  • Shot detection and results: TransNet V2 is preferable on ClipShots and BBC Planet Earth and comparable with other models on RAI.All compared methods perform worse on ClipShots, where the paper reports possible annotation omissions and incorrectly labeled transition frames.

2.4 Simple Usage Interface

The paper provides a simple interface for applying a trained TransNet V2 instance to videos and converting predictions into scenes. The example also visualizes predictions from both classification heads, although evaluation uses only the single-frame head.

  • Simple Usage Interface: The repository provides full training and evaluation code, trained TransNet V2 weights, and a simple inference API.The trained instance is available through the repository’s inference directory.
  • Simple Usage Interface: The usage example loads TransNet V2 weights and calls predict_video on a video path.The returned values include video frames, single-frame predictions, and all-frame predictions.
  • Simple Usage Interface: The interface converts model predictions into a list of scenes and supports visualization of the model’s predictions.The paper shows the visualization and scene list produced by the example code.
  • Simple Usage Interface: The visualization displays predictions from both heads, but evaluation uses only the green single-frame-head predictions.The two heads are shown with green and blue colors in the visualization.

3 CONCLUSION

The paper presents TransNet V2 as a deep network for common shot-transition detection and documents its architecture, training experience, evaluations, and usage. It concludes that the released pretrained component can support video-analysis pipelines requiring shot information.

  • CONCLUSION: TransNet V2 is presented as a deep network for detecting common shot transitions and as an initial step in video-analysis processes.The paper connects the architecture and training discussion with performance evaluations against recent deep learning approaches.
  • CONCLUSION: The released pretrained network and simple code examples are intended to support integration into multimedia search and analytics pipelines requiring shot information.The paper describes the software component as easily integrable into video preprocessing frameworks.
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