Closed-Circuit Television Data as an Emergent Data Source for Urban Rail Platform Crowding Estimation

Occupancy estimation and crowd counting are critical tasks in designing smart and efficient public transport vehicles. Given that public transport loading can vary from sparse to crowded, classical models for occupancy estimation must be adapted to suit this purpose. Attention mechanisms have shown remarkable capability in enhancing the representational power of deep neural networks for crowd counting in congested scenes with occlusion, complex backgrounds, and perspective distortion. However, conventional approaches, often implemented as parameterized sub-networks within convolutional layers, inevitably increase model size and computational cost, limiting deployment on resource-constrained edge devices. This paper investigates the effectiveness of state-of-the-art parameter-free attention mechanisms for crowd counting and density map estimation in highly congested scenes. We evaluate channel-wise (PFCA), spatial-wise (SA), and 3-D (SimAM) modules and compare their performance with parameterized attention modules constrained to introduce no more than 1% additional parameters. Furthermore, we present a novel combination of attention mechanisms that combines the strengths of PFCA and SA (PFCASA) customized for analyzing video streams onboard public transport systems. Using CSRNet as the backbone, experiments on the ShanghaiTech dataset demonstrate that parameter-free attention mechanisms achieve comparable or superior accuracy without introducing additional model parameters. A detailed performance analysis further reveals that PFCASA outperforms other attention modules in scenes with fewer than 40 individuals, while PFCA shows greater effectiveness as crowd density increases, underscoring their potential applicability for integration into smart public transport modalities.

Our primary objective is to advance video object counting in crowded scenes, aiming to robustly count all instances of a target category based on given text or visual prompts. Existing methods rely on RGB information, limiting their discriminative ability in crowded and occluded conditions. To address this, we propose a Depth-Guided Detector (DG-Det) along with a general post-processing pipeline. By integrating depth cues with multi-scale RGB-D cross-attention and explicit occlusion prediction, our method enhances spatial understanding and achieves robust detection in crowded and occluded scenes. Furthermore, we introduce a unified de-duplication framework to eliminate cross-frame redundant counting. To facilitate future research, we also release a new RGB-D Video Object Counting dataset featuring depth information and multiple object categories persequence. Extensive experiments demonstrate that our method achieves a 62.01% reduction in MAE compared to existing baselines, and also produces consistent improvements in RMSE. We provide the source code at https://github.com/streamer-AP/DG-Net and the dataset at https://huggingface.co/datasets/aerospace123/RGBD-VideoCount.

In real-world applications, pedestrian trajectory prediction models rely on inputs from detection and tracking systems. Prior trajectory prediction benchmarks either contain relatively sparse pedestrian interactions, assume perfect tracking inputs, or rely on overhead viewpoints that minimize occlusion and perspective distortion, limiting evaluation in realistic dense-crowd scenarios. We present CrowdTraj, a benchmark for pedestrian trajectory prediction in natural dense crowd scenes. Unlike previous datasets, CrowdTraj supports end-to-end evaluation from detection through tracking to trajectory prediction under severe occlusion in CCTV views. It also captures diverse, natural pedestrian behaviours, including abrupt directional changes rarely observed in existing benchmarks. CrowdTraj includes five diverse scenes, with an average of 1,146 unique pedestrians per scene, maximum frame-level densities ranging from 114 to 372 pedestrians, and over 3.2 million annotated head bounding boxes. CrowdTraj provides pixel and real-world coordinates via per-scene homography matrices for physically meaningful analysis. Our experimental results show that tracking accuracy (IDF1) drops to 0.68 to 0.70 in the densest scenes, compared with approximately 0.90 in less crowded scenes. Trajectory prediction training also becomes substantially more computationally expensive in dense scenes, with training times increasing by up to 8 times. These findings show that CrowdTraj exposes limitations in current trajectory prediction pipelines that remain hidden on existing sparse-crowd benchmarks, particularly in robustness to tracking noise and computational scalability.