arXiv subjects

Sarvin Nami

Publications and source records attributed to Sarvin Nami.

3 recordsLinked to original sources

arXiv2026

From White Canes to Edge AI: A Survey of Assistive Navigation Technologies for Blind and Low-Vision Individuals

Independent mobility is a foundational requirement for the autonomy of blind and low-vision (BLV) individuals, yet the assistive technologies that support it have historically evolved slowly. This survey systematically reviews 108 peer-reviewed studies published between 2008 and 2026, organizing the field into four technological eras: (i) a foundational era of passive mechanical aids, namely the white cane and the guide dog; (ii) a hardware era of electronic travel aids built on ultrasonic, infrared, and satellite positioning sensors that alert users to proximity but lack semantic understanding; (iii) a deep learning era in which convolutional and transformer-based detectors introduced context-aware scene understanding, initially tethered to cloud infrastructure; and (iv) a contemporary edge-native era defined by model compression, quantization, hardware acceleration, and multi-sensor fusion that relocate intelligence onto resource-constrained wearable and mobile devices. For each era we analyze representative systems, sensing modalities, computational architectures, and reported performance, and we consolidate the field's model-optimization techniques into a single technical reference spanning four detector families and twelve compression strategies. Across the corpus, we find a persistent mismatch between rapid algorithmic progress and slow real-world adoption, driven by cognitive overload in user interfaces, fragmented evaluation protocols, and the scarcity of studies involving BLV participants. We conclude with open challenges, including seamless indoor-outdoor transition, standardized benchmarks, privacy-preserving perception, and the emerging role of multimodal foundation models, and outline a roadmap for socially acceptable, user-centered assistive navigation.

cs.HC↗

arXiv2026

ROD-Dataset: A Pedestrian-Perspective Benchmark of Urban Obstacle Classes for YOLO-Based Detection

Two groups of pedestrians face avoidable risk on the city sidewalk: people living with visual impairment, who reach an obstacle before anything warns them, and people walking with divided attention, who register a hazard late or not at all. A vision system can warn them only about obstacles it has learned to recognize, so its training data limits the protection it offers. No suitable training set exists: public benchmarks are recorded from vehicles, aerial platforms, or fixed rigs, general-purpose detectors omit the street furniture that causes trips, and pedestrian-focused collections are confined to single regions with incompatible class schemes. This paper introduces the Real-time Obstacle Detection (ROD) dataset: more than 24,000 images and 40,000 annotated obstacles across 25 classes, from vehicles and people to the stairs, manholes, traffic cones, and benches that general-purpose detectors overlook. Because street furniture differs between cities, ROD unifies twenty-nine public collections onto one taxonomy and adds eye-level imagery from two contrasting cities on two continents, Tehran and Toronto. Every label is human-verified, and each capture campaign is admitted only after a compact detector shows it can learn the class. Six lightweight YOLO detectors (nano and tiny variants) benchmarked on ROD all exceed 0.86 recall; YOLO12n attains the highest recall (0.889), YOLO26n the highest precision (0.925), and YOLOv9t the best mAP@0.50 (0.882) with the fewest parameters. Because the recall and mAP rankings disagree, selecting a model by mAP does not minimize missed obstacles, so per-class recall is reported for all 25 classes.

cs.CV↗

arXiv2026

Real-Time Obstacle Detection and Distance Estimation on Smartphones for Visually Impaired and Distracted Pedestrians

According to the World Health Organization, at least 2.2 billion people worldwide live with visual impairment, including tens of millions with severe vision loss. Traditional aids such as white canes or guide dogs remain costly, limited in capability, and inaccessible to many. At the same time, smartphone distraction in public spaces has been linked to elevated risks of collisions with vehicles, trips, and falls. These dual challenges motivate lightweight, smartphone-based solutions that combine accessibility with low latency. We present a Kotlin-based Android application that uses a YOLOv8 detector, retrained to recognize eighteen common urban obstacle classes, to deliver real-time obstacle alerts for both user groups. The detector was optimized with pruning, quantization, and early-exit branches, and deployed through the Android Neural Networks API (NNAPI), which can dispatch inference to GPU, NPU, or DSP accelerators when available. On a mid-range smartphone, these optimizations reduced average inference latency from about 3.2 s for the off-the-shelf YOLOv8n to 0.39-0.48 s; the recommended configuration (INT8 quantization with early exit) runs in 0.42-0.48 s while retaining a recall of 0.81. The application combines the optimized detector with ARCore-based or monocular focal-length distance estimation, so it works with a single smartphone camera and requires no LiDAR, depth camera, or other specialized sensor. Multimodal feedback, including Text-to-Speech guidance for visually impaired users and vibration alerts for distracted pedestrians, supports use across different contexts.

cs.CV↗