Accurately predicting pedestrian crossings in front of ego-vehicles is essential for intelligent transportation systems (ITS) to enhance road safety. Many existing approaches rely on multiple input modalities, such as scene images, segmentation maps, and trajectory data, which introduce complexity and inefficiencies, thus limiting real-time applicability. To address these challenges, we propose PedGT, a graph-based transformer model that integrates a graph convolutional network (GCN) for spatial feature extraction and a transformer encoder for temporal modeling. Unlike multi-modal methods, PedGT simplifies the pipeline by utilizing only pedestrian pose keypoints and bounding box center points, achieving superior performance on two benchmark datasets. On PIE, it achieves an F1 score of 91% and a recall of 93%, surpassing the previous best of 89%, and 88% by PCPNet. On JAAD, PedGT improves F1 and recall to 70%, outperforming PedFormer's 54% and 48%. Ablation studies highlight the impact of data normalization on accuracy, while frame importance analysis identifies keyframes influencing predictions. This work demonstrates that selecting optimal inputs and leveraging an efficient spatial-temporal model enable PedGT to outperform multi-modal solutions, providing a more streamlined and effective approach for pedestrian intention prediction.

Accurately predicting whether pedestrians will cross in front of an autonomous vehicle is essential for ensuring safe and comfortable maneuvers. However, developing models for this task remains challenging due to the limited availability of diverse datasets containing both crossing (C) and non-crossing (NC) scenarios. Therefore, we propose a procedure that leverages synthetic videos with C/NC labels and an untrained model whose architecture is designed for C/NC prediction to automatically produce C/NC labels for a set of real-world videos. Thus, this procedure performs a synth-to-real unsupervised domain adaptation for C/NC prediction, so we term it S2R-UDA-CP. To assess the effectiveness of S2R-UDA-CP in self-labeling, we utilize two state-of-the-art models, PedGNN and ST-CrossingPose, and we rely on the publicly-available PedSynth dataset, which consists of synthetic videos with C/NC labels. Notably, once the real-world videos are self-labeled, they can be used to train models different from those used in S2R-UDA-CP. These models are designed to operate onboard a vehicle, whereas S2R-UDA-CP is an offline procedure. To evaluate the quality of the C/NC labels generated by S2R-UDA-CP, we also employ PedGraph+ (another literature referent) as it is not used in S2R-UDA-CP. Overall, the results show that training models to predict C/NC using videos labeled by S2R-UDA-CP achieves performance even better than models trained on human-labeled data. Our study also highlights different discrepancies between automatic and human labeling. To the best of our knowledge, this is the first study to evaluate synth-to-real self-labeling for C/NC prediction.

The main goal of this work was to implement a reliable machine learning algorithm that can classify a dog's age given only a photograph of its face.The problem, which seems simple for humans, presents itself as very difficult for the machine learning algorithms due to differences in facial features among the dog population.As convolutional neural networks (CNNs) performed poorly in this problem, the authors took another approach of creating novel architecture consisting of a combination of CNN and vision transformer (ViT) and examining the age of the dogs separately for every breed.Authors achieved better results than those in initial works covering the problem.

This paper presents the analysis of the impact of a floating-point number precision reduction on the quality of text classification. The precision reduction of the vectors representing the data (e.g. TF-IDF representation in our case) allows for a decrease of computing time and memory footprint on dedicated hardware platforms. The impact of precision reduction on the classification quality was performed on 5 corpora, using 4 different classifiers. Also, dimensionality reduction was taken into account. Results indicate that the precision reduction improves classification accuracy for most cases (up to 25% of error reduction). In general, the reduction from 64 to 4 bits gives the best scores and ensures that the results will not be worse than with the full floating-point representation.

This paper focuses on an examination of an applicability of Recurrent Neural Network models for detecting anomalous behavior of the CERN superconducting magnets. In order to conduct the experiments, the authors designed and implemented an adaptive signal quantization algorithm and a custom Gated Recurrent Unit-based detector and developed a method for the detector parameters selection. Three different datasets were used for testing the detector. Two artificially generated datasets were used to assess the raw performance of the system whereas the dataset intended for real-life experiments and model training was composed of the signals acquired from a new type of magnet, to be used during High-Luminosity Large Hadron Collider project. Several different setups of the developed anomaly detection system were evaluated and compared with state-of-the-art One Class Support Vector Machine (OC-SVM) reference model operating on the same data. The OC-SVM model was equipped with a rich set of feature extractors accounting for a range of the input signal properties. It was determined in the course of the experiments that the detector, along with its supporting design methodology, reaches F1 equal or very close to 1 for almost all test sets. Due to the profile of the data, the setup with the lowest maximum false anomaly length of the detector turned out to perform the best among all five tested configuration schemes of the detection system. The quantization parameters have the biggest impact on the overall performance of the detector with the best values of input/output grid equal to 16 and 8, respectively. The proposed solution of the detection significantly outperformed OC-SVM-based detector in most of the cases, with much more stable performance across all the datasets.

This paper focuses on analyzing a Spatial Pooler (SP) of Hierarchical Temporal Memory (HTM) ability for facilitating object classification in noisy video streams.In particular, we seek to determine whether employing SP as a component of the video system increases overall robustness to noise.We have implemented our own version of HTM and applied it to object recognition tasks under various testing conditions.The system is composed of a video preprocessing block, a dimensionality reduction section which contains SP, a histograms collecting module and SVM classifier.Our experiments involve assessing performance of two different system setups (i.e. a version featuring SP and one without it) under various noise conditions with 32-frame video files.In order to make tests fair and repeatable the videos of several 3-D geometric shapes were artificially generated.Subsequently, Gaussian noise of a different intensity was introduced to the videos making them more indistinct.Such an approach mimics real-life scenarios where the system is taught ideal objects and then faces in its normal working conditions the challenge of detecting noisy ones.The results of the experiments reveal the superiority of the solution featuring Spatial Pooler over the one without it.Furthermore, the system with SP performed better also in the experiment without a noise component introduced and achieved a mean F1-score of 0.91 in ten trials.

The presented algorithms employ the Vector Space Model (VSM) and its enhancements such as TFIDF (Term Frequency Inverse Document Frequency). Vector space model suffers from curse of dimensionality. Therefore various dimensionality reduction algorithms are utilized. This paper deals with two of the most common ones i.e. Latent Semantic Indexing (LSI) and Random Projection (RP). It turns out that the size of a document corpus has a substantial impact on the processing time. Thus the authors introduce GPU based on acceleration of these techniques. A dedicated test set-up was created and a series of experiments were conducted which revealed important properties of the algorithms and their accuracy. They show that the random projection outperforms LSI in terms of computing speed at the expanse of results quality.

Most presented implementations of the exponential function confine to the single precision format. Increasing data width to the double precision format requires a different approach. The presented novel architecture employs three independent Look-Up Tables (LUTs) together with a short Taylor expansion exp(x)≈1+x. Implementation results show that the double precision exp() function implementation achieves huge performance with satisfactory accuracy, latency and FPGA area consumption.