Hari Kalva

Md Eimran Hossain Eimon, Velibor Adzic, Hari Kalva et al.

Machines are increasingly becoming the primary consumers of visual data, yet most deployments of machine-to-machine systems still rely on remote inference where pixel-based video is streamed using codecs optimized for human perception. Consequently, this paradigm is bandwidth intensive, scales poorly, and exposes raw images to third parties. Recent efforts in the Moving Picture Experts Group (MPEG) redesigned the pipeline for machine-to-machine communication: Video Coding for Machines (VCM) is designed to apply task-aware coding tools in the pixel domain, and Feature Coding for Machines (FCM) is designed to compress intermediate neural features to reduce bitrate, preserve privacy, and support compute offload. Experiments show that FCM is capable of maintaining accuracy close to edge inference while significantly reducing bitrate. Additional analysis of H.26X codecs used as inner codecs in FCM reveals that H.265/High Efficiency Video Coding (HEVC) and H.266/Versatile Video Coding (VVC) achieve almost identical machine task performance, with an average BD-Rate increase of 1.39% when VVC is replaced with HEVC. In contrast, H.264/Advanced Video Coding (AVC) yields an average BD-Rate increase of 32.28% compared to VVC. However, for the tracking task, the impact of codec choice is minimal, with HEVC outperforming VVC and achieving BD Rate of -1.81% and 8.79% for AVC, indicating that existing hardware for already deployed codecs can support machine-to-machine communication without degrading performance.

Jaewon Lee, Md Eimran Hossain Eimon, Avinash Srinivasan et al.

Digital forensic investigations often face significant challenges when recovering fragmented multimedia files that lack file system metadata. While traditional file carving relies on signatures and discriminative deep learning models for fragment classification, these methods cannot reconstruct or predict missing data. We propose a generative approach to multimedia carving using bGPT, a byte-level transformer designed for next-byte prediction. By feeding partial BMP image data into the model, we simulate the generation of likely fragment continuations. We evaluate the fidelity of these predictions using different metrics, namely, cosine similarity, structural similarity index (SSIM), chi-square distance, and Jensen-Shannon divergence (JSD). Our findings demonstrate that generative models can effectively predict byte-level patterns to support fragment matching in unallocated disk space.

Hayat Ullah, Abbas Khan, Arslan Munir et al.

Realistic human surveillance datasets are crucial for training and evaluating computer vision models under real-world conditions, facilitating the development of robust algorithms for human and human-interacting object detection in complex environments. These datasets need to offer diverse and challenging data to enable a comprehensive assessment of model performance and the creation of more reliable surveillance systems for public safety. To this end, we present two visual object detection benchmarks named OD-VIRAT Large and OD-VIRAT Tiny, aiming at advancing visual understanding tasks in surveillance imagery. The video sequences in both benchmarks cover 10 different scenes of human surveillance recorded from significant height and distance. The proposed benchmarks offer rich annotations of bounding boxes and categories, where OD-VIRAT Large has 8.7 million annotated instances in 599,996 images and OD-VIRAT Tiny has 288,901 annotated instances in 19,860 images. This work also focuses on benchmarking state-of-the-art object detection architectures, including RETMDET, YOLOX, RetinaNet, DETR, and Deformable-DETR on this object detection-specific variant of VIRAT dataset. To the best of our knowledge, it is the first work to examine the performance of these recently published state-of-the-art object detection architectures on realistic surveillance imagery under challenging conditions such as complex backgrounds, occluded objects, and small-scale objects. The proposed benchmarking and experimental settings will help in providing insights concerning the performance of selected object detection models and set the base for developing more efficient and robust object detection architectures.