Browsing by Author "Mohanarangan, K"

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    Confidence-Aware Cascaded Learning for Real-Time Weakly Supervised Anomaly Detection in Surveillance Video
    (Institute of Electrical and Electronics Engineers Inc., 2026-04-01) Mohanarangan, K; Palanisamy, P
    Weakly supervised video anomaly detection (WS-VAD) has advanced significantly, yet most approaches remain tailored to long, untrimmed streams and fail to meet the latency and reliability demands of real-world surveillance. In asset-protection scenarios, where motion-triggered clips must be processed in real-time, existing methods often compromise either accuracy or efficiency. We present a Confidence-Aware Sequential Routing Ensemble (CASRE), a cascaded framework designed for Clip-Level Weak Supervision (CLWS). CASRE integrates a temperature-scaled ResNet3D (TDR3D) primary detector with a novel Temporal-Scale Delta Loss to enforce confidence separation. Only low-confidence clips are routed to a transformer-based MViTv2 fallback, and final predictions are obtained through calibrated probabilistic fusion. This selective routing ensures that high-capacity models are invoked for fewer than 6% of clips, while the majority benefit from lightweight inference. Extensive evaluation on UCF–Crime, UCSD–Ped1/Ped2, and the newly introduced SSAD dataset, a real-world benchmark curated for asset-protection surveillance, demonstrates that CASRE achieves up to 98.64% accuracy and 99.91% AUC, while maintaining sub-0.03 s inference for most samples. By combining confidence calibration, selective routing, and heterogeneous backbone fusion, CASRE establishes a deployable WS-VAD paradigm that balances accuracy, robustness, and computational efficiency, making it well-suited for large-scale surveillance deployments under strict operational constraints.
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    Disagreement-aware late fusion of 3DCNN and MViT for clip-level video anomaly detection
    (Springer Science and Business Media B.V., 2026-06-30) Mohanarangan, K; Palanisamy, P
    We propose Adaptive Late Router with Temperature Scaling (ALeRT), an inference-only, disagreement-aware late fusion framework for clip-level video anomaly detection. ALeRT combines two complementary experts: MMAnet, a lightweight multi-scale 3D CNN for fine-grained motion dynamics, and MViT, a Kinetics-pretrained multiscale Vision Transformer for long-range spatiotemporal modeling, without requiring joint retraining. Model outputs are calibrated using temperature scaling and fused via a Product-of-Experts (PoE) to reinforce agreement, while disagreement is resolved using confidence margins and per-class Positive Predictive Value (PPV). Temporal consistency is further enhanced through exponential moving average (EMA) smoothing. The framework is evaluated across multiple random seeds, reporting mean performance with low variance, demonstrating strong robustness and generalization. Experiments on LPS (Loss Preventive Surveillance), UCF-Crime-Clips (UCF-CC), and Virat2-RC (ReClassified) datasets show consistent improvements over individual backbones. Notably, ALeRT achieves these gains with only 3.1 ms fusion overhead per clip, enabling real-time inference (5.7–6.0 events/s, maximum of 300 FPS(FPS (frames per second) indicates the number of video frames processed per second.)) on commodity GPUs. These results highlight the effectiveness of disagreement-aware fusion for scalable and deployment-ready video anomaly detection.
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    TriPath3DNet: an efficient real-time model for multi-class classification in real-life surveillance videos of fixed duration
    (Nature Research, 2026-06-17) Mohanarangan, K; Palanisamy, P
    This paper presents TriPath3DNet, a novel, efficient, and interpretable 3D CNN architecture designed for real-time, multi-class classification of short, motion-triggered surveillance video clips under challenging real-world conditions–including occlusion, variable lighting, adverse weather, and subtle low-motion anomalies such as loitering or zone intrusion. TriPath3DNet integrates three complementary temporal pathways–short-term motion, long-term context, and Temporal Difference Encoding (TDE)–into a lightweight ResNet3D (R3D)-18 backbone to jointly model transient dynamics and sustained activities. Evaluated on four datasets–including the newly curated Virat1-RC, Virat2-RC, UCF-Crime, and our proprietary In-House Dataset (IHD)–TriPath3DNet achieves state-of-the-art or near state-of-the-art performance, with up to 95.37% accuracy, 99.42% AUC, and an inference latency of 129 to 137 ms per 50-frame clip (approx 2.6 ms per frame) on an 11 GB GPU, using only 33.46 M parameters. Notably, it outperforms both CNN- and transformer-based baselines–including MViTv1, MViTv2, and VideoSwin–by significant margins, especially on anomaly-dense benchmarks like UCF-Crime, where most vision transformers struggle. While MViTv2 achieves slightly higher accuracy on IHD (91.87% vs. 89.46%), TriPath3DNet delivers substantially better AUC (98.07% vs. 90.96%), indicating superior calibration for critical anomaly detection. Ablation studies confirm that each temporal branch contributes meaningfully to performance, and Grad-CAM visualizations demonstrate spatially precise and temporally coherent attention maps. By aligning architectural design with edge–cloud deployment constraints and the operational realities of industrial surveillance, our work bridges the gap between academic research and real-world video analytics.