Italy

The future civilian, and professional autonomous vehicles to be realised into the market should apprehend and interpret the road in a manner similar to the human drivers. In structured urban environments where signs, road lanes and markers are well defined and ordered, landmark-based road tracking and localisation has significantly progressed during the last decade with many autonomous vehicles to make their debut into the market. However, in semi-structured and rural environments where traffic infrastructure is deficient, the autonomous driving is hindered by significant challenges. The paper at hand presents a Lidar-based method for road boundaries detection suitable for a service robot operation in rural and semi-structured environments. Organised Lidar data undergo a spatial distribution processing method to isolate road limits in a forward looking horizon ahead of the robot. Stereo SLAM is performed to register subsequent road limits and RANSAC is applied to identify edges that correspond to road segments. In addition, the robot traversable path is estimated and progressively merged with Bézier curves to create a smooth trajectory that respects vehicle kinematics. Experiments have been conducted on data collected from our robot on a semi-structured urban environment, while the method has also been evaluated on KITTI dataset exhibiting remarkable performance.

ICPR 2020

PRRoad tracking in semi-structured environments using spatial distribution of Lidar data

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


technical paper

Breathing rate together with heart rate, blood pressure,
and body temperature, are considered as essential and vital
signs of human body. In this demo, we present a novel system to
estimate the respiratory rate using a low-cost, non-intrusive, and
passive infrared thermopile sensor array. The thermopile sensor
array can detect the thermal infrared energy and generate a
low-resolution video from subjects within its viewing range. We
collected low resolution 8 8-pixel thermal video clips from 40
subjects at resting position from a distance of one-meter between
the thermopile sensor array and the subjects. We processed
the image frames using Haar-like filters and generated a one-dimensional
feature signal from each video. The signal exhibits
an almost periodic behavior following the respiratory movements.
A peak detection algorithm is used to detect each breath. The
proposed method is computationally efficient because it is based
on multiplierless Haar-type feature extraction and peak detection.
We achieved a high positive correlation rate of 97.77% between
the actual respiratory rate that was measured by monitoring
the chest movements during inhalation and exhalation and the
estimated rate in our dataset. We will demonstrate this IR array
based respiration rate detection system during the demo session.

Respiratory rate estimation using a low-resolution infrared sensor array

Feathers are fascinating natural components of birds and other, extinct dinosaurs. They exhibit a variety of crucial biological properties that are studied in multiple fields. Here we present work in visual analysis of feathers that leverages image-processing methods to observe important physical data about the structure and shape of feathers. Such data may be used to inform ornithology or paleontology as well as used to synthesize accurate computerized representations of feathers for high-fidelity visualization and interactive rendering.

Computer-Aided Visual Analysis of Feathers

This paper proposes a system for pose estimation on monkeys, in diverse and challenging scenarios, and under complex social interactions by using OpenPose. In comparison to most animals used for research, Monkeys present additional difficulties for pose estimation. Multiple degrees of freedom, unique complex postures, intricated social interactions, among others. Our monkey OpenPose trained model is robust against these difficulties. It achieves similar performance as in human pose estimation models, and it can run in real-time.

Multiple Monkey Pose Estimation Using OpenPose

This work describe an algorithm for the automatic analysis of the waggle dance of honeybees. The algorithm analyses a video of a beehive with 13,624 frames, acquired at 25 frames/second. The algorithm employs the following traditional image processing steps: conversion to grayscale, low pass filtering, background subtraction, thresholding, tracking and clustering to detect run of bees that perform waggle dances. The algorithm detected 44,530 waggle events, i.e. one bee waggling in one time frame, which were then clustered into 511 waggle runs. Most of these were concentrated in one section of the hive. The accuracy of the tracking was 90% and a series of metrics like intra-dance variation in angle and duration were found to be consistent with literature. Whilst this algorithm was tested on a single video, the
ideas and steps, which are simple as compared with Machine and Deep Learning techniques, should be attractive for researchers in this field who are not specialists in more complex techniques.

Automatic Analysis of Bees’ Waggle Dance

We investigate the task of entity counting in overhead imagery from the perspective of re-purposing representations learned from ground imagery, e.g., ImageNet, via feature adaptation. We explore two directions of feature adaptation and analyze their performances using two popular aerial datasets for vehicle counting: PUCPR+ and CARPK. First, we explore proxy self-supervision tasks such as RotNet, jigsaw, and image inpainting to re-fine the pretrained representation. Second, we insert additional network layers to adaptively select suitable features (e.g., squeeze and excitation blocks) or impose desired properties (e.g., using active rotating filters for rotation invariance). Our experimental results show that different adaptations produce different amounts of performance improvements depending on data characteristics.

Fine-tuning for one-look regression vehicle counting in low-shot aerial datasets

3D city-scale point cloud stitching is a critical component for large data collection, environment change detection, in which massive amounts of 3D data are captured under different times and conditions. 
This paper proposes a novel point cloud stitching approach, that automatically and accurately stitches multiple city-scale point clouds, which only share relatively small overlapping areas, into one single model for a larger geographical coverage. The proposed method firstly employs 2D image mosaicking techniques to estimate 3D overlapping areas among multiple point clouds, then applies 3D point cloud registration techniques to estimate the most accurate transformation matrix for 3D stitching.
The proposed method is quantitatively evaluated on city-scale reconstructed point cloud dataset and real-world city LiDAR dataset, in which, our method outperforms other competing methods with significant margins and achieved the highest precision score, recall score, and F-score.
Our method makes an important step towards automatic and accurate city-scale point cloud data stitching, which could be used in a variety of applications.

City-Scale Point Cloud Stitching Using 2D/3D Registration for Large Geographical Coverage

Motion imagery interpretability is commonly represented by the video National Imagery Interpretability Rating Scale (VNIIRS), which is a subjective metric based on human analysts’ visual assessment. Therefore, VNIIRS is a very time-consuming task. This paper presents the development of a fully automated motion imagery interpretability prediction, called AMIIP. AMIIP employs a three-dimensional convolutional neural network (3D-CNN) that accepts as inputs many video blocks (small image sequences) extracted from motion imagery and outputs the classified label for each video block. The result is a histogram of the labels/categories that is then used to estimate the interpretability of the motion imagery. For each training video clip, it is labeled based on its subjectively rated VNIIRS level; thus, the required human annotation for training data is minimized. By using a collection of 76 high definition aerial video clips, three preliminary experimental results indicate that the estimation error is within 0.5 VNIIRS rating scale

On the development of a classification based automated motion imagery interpretability prediction system

Current AI machine learning approaches perform well with similar sensors for data collection, training, and testing. The ability to learn and analyze data from multiple sources would enhance capabilities for AI systems. This paper presents a deep learning-based multi-source self-correcting approach to fuse data with different modalities. The data-level fusion approach maximizes the capability to detect unanticipated events/targets augmented with machine learning methods. The proposed Domain Adaptation for Efficient Learning Fusion (DAELF) deep neural network adapts to changes of the input distribution allowing for self-correcting of multiple source classification and fusion. When supported by a distributed computing hierarchy, the proposed DAELF scales up in neural network size and out in geographical span. The design of DAELF includes various types of data fusion, including decision-level and feature-level data fusion. The results of DAELF highlight that feature-level fusion outperforms other approaches in terms of classification accuracy for the digit data and the Aerial Image Data analysis

Deep Learning Based Domain Adaptation with Data Fusion for Aerial Image Data Analysis

Full Motion Video (FMV) and Wide Area Motion Imagery (WAMI) systems have been used extensively in the recent years to collect overhead imagery to produce value-added geospatial products. In order to better understand the limitations of these systems and to define the best flight conditions under which they can be operated, an integrated modeling and simulation (M&S) framework named Sensair was developed. This paper presents how this tool can be leveraged to simulate data collections with airborne camera systems using different parameters under varied conditions. Sensair can simulate realistic large-scale environments with moving vehicles and generate representative FMV and WAMI high-resolution imagery. The built-in interactive analysis tools enable the study of the different factors impacting the application of computer vision algorithms to aerial images. This paper presents Sensair’s M\&S capabilities and describes use cases where the framework is used to perform tasks that include 3D reconstruction and the generation of an image dataset to support the evaluation of vehicle detection approaches.

Modeling and Simulation Framework for Airborne Camera Systems

We propose a novel unsupervised approach based on a two-stage object-centric adversarial framework that only needs object regions for detecting frame-level local anomalies in videos. The first stage consists in learning the correspondence between the current appearance and past gradient images of objects in scenes deemed normal, allowing us to either generate the past gradient from current appearance or the reverse. The second stage extracts the partial reconstruction errors between real and generated images (appearance and past gradient) with normal object behaviour, and trains a discriminator in an adversarial fashion. In inference mode, we employ the trained image generators with the adversarially learned binary classifier for outputting region-level anomaly detection scores. We tested our method on four public benchmarks, UMN, UCSD, Avenue and ShanghaiTech and our proposed object-centric adversarial approach yields competitive or even superior results compared to state-of-the-art methods.

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