Scene classification in videos typically combines computer vision techniques to analyze spatial and temporal patterns. The core approach involves extracting visual features from video frames and modeling temporal relationships across frames. Methods range from traditional feature engineering to deep learning architectures designed for sequential data. Below are key techniques used in this domain.
1. Frame-Based Feature Extraction with CNNs Convolutional Neural Networks (CNNs) are widely used to extract spatial features from individual video frames. Pre-trained models like ResNet or VGG process each frame to capture textures, objects, and scene layouts. For example, a beach scene might be identified by features like sand, water, and sky detected in key frames. To handle videos, these frame-level features are often aggregated using techniques like average pooling or concatenation. However, this approach treats videos as unordered image sets, ignoring motion cues. To compensate, some implementations sample frames at fixed intervals or use attention mechanisms to weight important frames (e.g., focusing on a frame showing waves for a beach classification).
2. Temporal Modeling with 3D CNNs and RNNs To model motion, 3D CNNs extend traditional 2D convolutions by adding a temporal dimension. For instance, the C3D architecture uses 3x3x3 kernels to process short video clips, capturing spatiotemporal patterns like flowing water or moving vehicles. Alternatively, Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks analyze sequences of frame features. A common pipeline uses a CNN to extract per-frame features, followed by an LSTM to track scene changes over time. For example, a “park” scene might be recognized by combining static features (trees, benches) with motion (people walking). Hybrid architectures like Two-Stream Networks further improve accuracy by processing RGB frames and optical flow (motion vectors) in parallel, then fusing the outputs.
3. Transformers and Modern Architectures Recent approaches use vision transformers (ViTs) adapted for video. These split frames into patches, apply self-attention across both spatial and temporal dimensions, and classify scenes by global context. Models like TimeSformer divide attention computation into spatial and temporal steps, reducing computational cost. For example, a “concert” scene could be identified by detecting stage lights (spatial) and flashing patterns (temporal). Pretraining on large datasets like Kinetics-400 helps these models generalize. Practical implementations often combine ViTs with lightweight temporal modules (e.g., shifted windows in Video Swin Transformer) to balance accuracy and speed. For deployment, developers might fine-tune these models on domain-specific data—like using a sports dataset to distinguish between “basketball” and soccer" scenes based on court layouts and player movements.
These techniques are often combined in production systems. For example, a video platform might use a 3D CNN for short clips, a transformer for long-range dependencies, and ensemble the results for final classification. The choice depends on computational constraints, dataset size, and required accuracy.