Abstract

With the increased importance of autonomous navigation systems has come an increasing need to protect the safety of Vulnerable Road Users (VRUs) such as pedestrians. Predicting pedestrian intent is one such challenging task, where prior work predicts the binary cross/no-cross intention with a fusion of visual and motion features. However, there has been no effort so far to hedge such predictions with humanunderstandable reasons. We address this issue by introducing a novel problem setting of exploring the intuitive reasoning behind a pedestrian’s intent. In particular, we show that predicting the ‘WHY’ can be very useful in understanding the ‘WHAT’. To this end, we propose a novel, reasonenriched PIE++ dataset consisting of multi-label textual explanations/reasons for pedestrian intent. We also introduce a novel multi-task learning framework called MINDREAD, which leverages a cross-modal representation learning framework for predicting pedestrian intent as well as the reason behind the intent. Our comprehensive experiments show significant improvement of 5.6% and 7% in accuracy and F1-score for the task of intent prediction on the PIE++ dataset using MINDREAD. We also achieved a 4.4% improvement in accuracy on a commonly used JAAD dataset. Extensive evaluation using quantitative/qualitative metrics and user studies shows the effectiveness of our approach.

Abstract

Prior works have addressed the problem of driver intention prediction (DIP) by identifying maneuvers after their onset. On the other hand, early anticipation is equally important in scenarios that demand a preemptive response before a maneuver begins. However, there is no prior work aimed at addressing the problem of driver action anticipation before the onset of the maneuver, limiting the ability of the advanced driver assistance system (ADAS) for early maneuver anticipation. In this work, we introduce Anticipating Driving Maneuvers (ADM), a new task that enables driver action anticipation before the onset of the maneuver. To initiate research in ADM task, we curate Driving Action Anticipation Dataset, DAAD, that is multi-view: in- and out-cabin views in dense and heterogeneous scenarios, and multimodal: egocentric view and gaze information. The dataset captures sequences both before the initiation and during the execution of a maneuver. During dataset collection, we also ensure to capture wide diversity in traffic scenarios, weather and illumination, and driveway conditions. Next, we propose a strong baseline based on a transformer architecture to effectively model multiple views and modalities over longer video lengths. We benchmark the existing DIP methods on DAAD and related datasets. Finally, we perform an ablation study showing the effectiveness of multiple views and modalities in maneuver anticipation

Abstract

This paper presents the competition report on Reading Documents through Aria Glasses (ICDAR 2024 RDTAG) held at the 18th International Conference on Document Analysis and Recognition (ICDAR 2024). From a mixed reality perspective, understanding the text in the world is of paramount importance. However, all day long, always on, machine perception devices like Aria Glasses pose a unique primary challenge of lower resolution due to their power and sensor constraints. Moreover, diverse everyday scenes like variations in the lighting conditions and reading positions further complicate the reading tasks. To address this, we propose a new dataset and a challenge. Specifically, we propose three novel tasks: Isolated Word Recognition in Low Resolution (Task A), Prediction of Reading Order (Task B), and Page Level Recognition (Task C). We provide new training and test sets consisting of document images captured by Aria Glasses while reading diverse documents in English under various everyday scenarios. Our aim is to engage researchers with prior experience in English language OCR, and to establish benchmarks contributing to the academic literature in this field. A total of thirty-three different teams from around the world registered for this competition, and twelve teams submitted their results along with algorithm details. The winning team, SRCB, achieved a 97.23% Character Recognition Rate (CRR) and a 90.45% Word Recognition Rate (WRR) for Task A: Isolated Word Recognition in Low Resolution. Team Gang-of-N won Task B: Prediction of Reading Order with a BLEU score of 0.0939. Team SRCB also won Task C: Page Level Recognition and Reading with a 77.44% average Page Level Character Recognition Rate (PCRR) and a 50.55% average Page Level Word Recognition Rate (PWRR).

Abstract

This paper presents a competition report on the Recognition and Visual Question Answer on Handwritten Documents towards deeper understanding of handwritten multilingual documents (ICDAR 2024-HWD) held at the 18th International Conference on Document Analysis and Recognition (ICDAR 2024). Documents are in English or Indian Languages. Earlier editions related to recognition of Indian handwriting were held in conjunction with ICFHR 2022 and ICDAR 2023. A related DocVQA task was held in DAS 2020. This edition proposes three main tasks: Isolated Word Recognition (Task A), Page Level Recognition and Reading (Task B), and Visual Question Answers on Handwritten Documents (Task A). While Task A was already part of our previous competitions, we bring in new data as part of this edition. Task B and Task C are novel additions for this year. By attracting researchers with experience in printed and handwritten documents, we aim to establish benchmarks that significantly contribute to the academic literature in this field. A total of thirty-two teams around the world registered for this competition. Among them, only ten teams submitted their results along with algorithm details. The winning team, TSNUK, achieved an average 98.00% Character Recognition Rate (CRR) and 94.26% Word Recognition Rate (WRR) across four languages for Task A: Isolated Word Recognition. IndependentOCR excelled in Task B: Page Level Recognition and Reading, with 76.32% average Page Level Character Recognition Rate (PCRR) and 62.57% average Page Level Word Recognition Rate. The team PA_VCG won Task C: Visual Question Answering on Handwritten Documents with a 0.643 ANLS score.

Abstract

Extensive research and the development of benchmark datasets have primarily focused on Scene Text Recognition (STR) in Latin languages. However, the scenario differs for Indian languages, where the complexities in syntax and semantics have posed many challenges, resulting in limited datasets and comparatively less research in this domain. Overcoming these challenges is crucial for advancing scene text recognition in Indian languages. Although a few works have touched upon this issue, they are constrained in the size and scale of the data as far as we know. To bridge this gap, this paper introduces a large scale, diverse dataset, named as IIIT-IndicSTR-Word for Indic scene text. Comprising a total of 250K word level images in ten different languages—Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Odia, Punjabi, Tamil, and Telugu, these images are extracted from roadside scenes captured by a GoPro camera. The dataset encompasses a wide array of realistic adversarial conditions, including blur, changes in illumination, occlusion, non-iconic texts, low resolution, and perspective text. We establish a baseline for the proposed dataset, facilitating evaluation and benchmarking with a specific focus on STR tasks. Our findings indicate that our dataset is a practical training source to enhance performance on respective datasets

Abstract

The practical applications of Handwritten Text Recognition (HTR) have flourished with many successful commercial APIs, solutions, and diverse use cases. Despite the availability of numerous industrial solutions, academic research in HTR, particularly for English, has been hindered by the scarcity of publicly accessible data. To bridge this gap, this paper introduces IIIT-HW-English-Word, a large and diverse collection of offline handwritten English documents. This dataset comprises unconstrained camera-captured images featuring 20,800 handwritten documents crafted by 1,215 writers. Within this dataset, covering 757,830 words, we identify 174,701 unique words encompassing a variety of content types, such as alphabetic, numeric, and stop-words. We also establish a baseline for the proposed dataset, facilitating evaluation and benchmarking, explicitly focusing on word recognition tasks. Our findings suggest that our dataset can effectively serve as a training source to enhance performance on respective datasets

Abstract

In the current digital era, about 80% of the digital data which is being generated is unstructured and unlabeled natural language text. In the development cycle of information retrieval and text mining applications, text representation is the most fundamental and critical step, as its effectiveness directly impacts the application’s performance. The existing traditional text representation frameworks are mostly frequency distribution-based. In this work, we explored the spatial distribution of word embeddings and proposed two text representational models. The experimental demonstrated that proposed models perform consistently better at text mining tasks compared to baseline methods.

Abstract

Purpose : Foveal Hypoplasia (FH) is a macular abnormality characterised by incomplete development of the foveal region. This abnormality is most commonly observed alongside rare inherited disorders such as albinism, but the precise mechanisms behind abnormal foveal development remain unclear. Thus, we aimed to identify genetic loci associated with FH, to provide insight into the genetic architecture of normal and abnormal foveal development. Methods : We developed a deep learning model to analyse retinal OCT scans from a subset of UK Biobank participants, classifying individuals of European descent into FH (n=4403) and control groups (n=30069). FH classification was then used to conduct a genome-wide association study (GWAS) using these 34,472 individuals, and associations were mapped to causative genes using single nuclei multi-omics data obtained from embryonic and foetal retinal tissue. Further prioritisation of candidate genes was sought by performing CRISPR-Cas9 mediated knockout of target genes in Zebrafish. Genes were prioritised based on the impact of gene knockouts on visual function in Zebrafish, assessed using the optokinetic response. Results : Our GWAS identified 44 genetic variants independently associated with FH (P<5×10-8), including 31 novel variants not previously linked to FH or related traits. These novel associations include variants in genes such as TYR and OCA2, known for their roles in FH in a Mendelian context, and 28 novel genes not previously associated with FH. The identified genes operate in various biological processes such as the melanin biosynthesis pathway, melanosome function, cell fate and temporal patterning. This aligns with the functional characteristics of causative FH genes. Conclusions : In the first GWAS study of FH, we uncovered novel genetic associations critical to understanding foveal development. The identified associations offer new avenues for research, providing mechanistic insight into the genetic factors underpinning the foveal region's development and the pathogenesis of FH.

Abstract

The present disclosure provides a system and a method for generating a secure digital certificate for a user using cryptographic hashing and storing the digital certificate on a smart contract by a decentralized blockchain. The method includes (i) generating a registration token for a first user using the cryptographic hashing,(ii) storing the registration token of the first user as a second hash in the smart contract,(iii) receiving digital request through the first user device,(iv) validating (a) the digital request of the first user by (m) authenticating whether the biometrics of the first user, and (n) checking whether the second hash exists in the decentralized blockchain, and (b) the second user associated with a second user device using a driver algorithm,(v) sending a message to the second user device to initiate the digital request and (vi) generating the digital certificate when the first user receives a verification.

Abstract

The present disclosure provides a system for detecting and neutralizing a plurality of security attacks on a blockchain network to improve security of the blockchain network. The system includes a sender node that is configured to send a transaction to a user device associated with at least one user. The blockchain network is configured to (i) receive the transaction from the sender node when the first block gets validated in the first blockchain,(ii) validate the transaction of the first block to neutralize the first attack,(iii) determine a second attack by initiating a split in the first blockchain to determine a second blockchain, and (iv) invalidate the second blockchain when the latest block of the first blockchain invalidates the second block of the second blockchain when a proof of invalidity (POI) is added to the second block to neutralize the second attack.