Person re-identification via efficient inference in fully connected CRF

In this paper, we address the problem of person re-identification problem, i.e., retrieving instances from gallery which are generated by the same person as the given probe image. This is very challenging because the person's appearance usually undergoes significant variations due to changes in illumination, camera angle and view, background clutter, and occlusion over the camera network. In this paper, we assume that the matched gallery images should not only be similar to the probe, but also be similar to each other, under suitable metric. We express this assumption with a fully connected CRF model in which each node corresponds to a gallery and every pair of nodes are connected by an edge. A label variable is associated with each node to indicate whether the corresponding image is from target person. We define unary potential for each node using existing feature calculation and matching techniques, which reflect the similarity between probe and gallery image, and define pairwise potential for each edge in terms of a weighed combination of Gaussian kernels, which encode appearance similarity between pair of gallery images. The specific form of pairwise potential allows us to exploit an efficient inference algorithm to calculate the marginal distribution of each label variable for this dense connected CRF. We show the superiority of our method by applying it to public datasets and comparing with the state of the art.Comment: 7 pages, 4 figure

Airport Passenger Processing Technology: A Biometric Airport Journey

A passengers’ traveling journey throughout the airport is anything but simple. A passenger goes through numerous hoops and hurdles before safely boarding the aircraft. Many airports today are implementing isolated solutions for passenger processing. Some of these technologies include automated self-service kiosks and bag tag, self-service bag drop-off, along with automated self-service gates for boarding and border control. These solutions can be integrated with biometric systems to enhance passenger handling. This thesis analyzes the current passenger processing technology implemented at airports around the world and their associated challenges that passengers face. A new passenger processing technology called a biometric single token identification (ID) is presented as a solution to help alleviate current issues. By using a medium-sized international airport as a case study, the results show that a single token ID is beneficial to the time it takes to process a passenger. Furthermore, it demonstrates that implementation of a single token ID with self-service technology can provide enhanced passenger travel experience, improving operational process efficiency, all while ensuring safety and security

Multitarget Tracking in Nonoverlapping Cameras Using a Reference Set

Tracking multiple targets in nonoverlapping cameras are challenging since the observations of the same targets are often separated by time and space. There might be significant appearance change of a target across camera views caused by variations in illumination conditions, poses, and camera imaging characteristics. Consequently, the same target may appear very different in two cameras. Therefore, associating tracks in different camera views directly based on their appearance similarity is difficult and prone to error. In most previous methods, the appearance similarity is computed either using color histograms or based on pretrained brightness transfer function that maps color between cameras. In this paper, a novel reference set based appearance model is proposed to improve multitarget tracking in a network of nonoverlapping cameras. Contrary to previous work, a reference set is constructed for a pair of cameras, containing subjects appearing in both camera views. For track association, instead of directly comparing the appearance of two targets in different camera views, they are compared indirectly via the reference set. Besides global color histograms, texture and shape features are extracted at different locations of a target, and AdaBoost is used to learn the discriminative power of each feature. The effectiveness of the proposed method over the state of the art on two challenging real-world multicamera video data sets is demonstrated by thorough experiments

A Machine Learning Approach to Safer Airplane Landings: Predicting Runway Conditions using Weather and Flight Data

The presence of snow and ice on runway surfaces reduces the available tire-pavement friction needed for retardation and directional control and causes potential economic and safety threats for the aviation industry during the winter seasons. To activate appropriate safety procedures, pilots need accurate and timely information on the actual runway surface conditions. In this study, XGBoost is used to create a combined runway assessment system, which includes a classifcation model to predict slippery conditions and a regression model to predict the level of slipperiness. The models are trained on weather data and data from runway reports. The runway surface conditions are represented by the tire-pavement friction coefficient, which is estimated from flight sensor data from landing aircrafts. To evaluate the performance of the models, they are compared to several state-of-the-art runway assessment methods. The XGBoost models identify slippery runway conditions with a ROC AUC of 0.95, predict the friction coefficient with a MAE of 0.0254, and outperforms all the previous methods. The results show the strong abilities of machine learning methods to model complex, physical phenomena with a good accuracy when domain knowledge is used in the variable extraction. The XGBoost models are combined with SHAP (SHapley Additive exPlanations) approximations to provide a comprehensible decision support system for airport operators and pilots, which can contribute to safer and more economic operations of airport runways

Unconstrained Face Detection and Open-Set Face Recognition Challenge

Face detection and recognition benchmarks have shifted toward more difficult environments. The challenge presented in this paper addresses the next step in the direction of automatic detection and identification of people from outdoor surveillance cameras. While face detection has shown remarkable success in images collected from the web, surveillance cameras include more diverse occlusions, poses, weather conditions and image blur. Although face verification or closed-set face identification have surpassed human capabilities on some datasets, open-set identification is much more complex as it needs to reject both unknown identities and false accepts from the face detector. We show that unconstrained face detection can approach high detection rates albeit with moderate false accept rates. By contrast, open-set face recognition is currently weak and requires much more attention.Comment: This is an ERRATA version of the paper originally presented at the International Joint Conference on Biometrics. Due to a bug in our evaluation code, the results of the participants changed. The final conclusion, however, is still the sam

From Categories to Individuals in Real Time — A Unified Boosting Approach

A method for online, real-time learning of individual-object detectors is presented. Starting with a pre-trained boosted category detector, an individual-object detector is trained with near-zero computational cost. The individual detector is obtained by using the same feature cascade as the category detector along with elementary manipulations of the thresholds of the weak classifiers. This is ideal for online operation on a video stream or for interactive learning. Applications addressed by this technique are reidentification and individual tracking. Experiments on four challenging pedestrian and face datasets indicate that it is indeed possible to learn identity classifiers in real-time; besides being faster-trained, our classifier has better detection rates than previous methods on two of the datasets

JB Soedirman Airport Sustainability Strategy to Encourage Regional Economic Strengthening

In the age of contemporary transportation, aviation is a significant subject. Developing nations or areas will increasingly need effective transportation. Significant economic developments are anticipated to benefit areas with airport infrastructure. This research discusses the responses of the community, including those of government officials and entrepreneurs, in Purbalingga Regency regarding the existence of Sudirman Airport which has been developed as a commercial airport and has been operating since 2021. In Purbalingga Regency, where Jenderal Besar Soedirman (JB Soedirman) Airport is located, this study seeks to understand how significant local communities believes the airport is to the region's ability to advance economically. The comprehensive interviewing of chosen respondents used in this study gives it a qualitative aspect. The findings of the study demonstrate that the presence of JB Soedirman Airport offers promising prospects for local development. The airport's presence indicates the potential for investment growth and business capitalization in Purbalingga Regency. The Purbalingga Regency government must provide the appropriate policy formulation to support the continuity and sustainability of the airport's operations in order to take advantage of the numerous favorable chance

Quantum surveillance and 'shared secrets'. A biometric step too far? CEPS Liberty and Security in Europe, July 2010

It is no longer sensible to regard biometrics as having neutral socio-economic, legal and political impacts. Newer generation biometrics are fluid and include behavioural and emotional data that can be combined with other data. Therefore, a range of issues needs to be reviewed in light of the increasing privatisation of ‘security’ that escapes effective, democratic parliamentary and regulatory control and oversight at national, international and EU levels, argues Juliet Lodge, Professor and co-Director of the Jean Monnet European Centre of Excellence at the University of Leeds, U

Introduction To \u27Artificial Intelligence In Failure Analysis Of Transportation Infrastructure And Materials\u27

Transportation infrastructures, including roads, bridges, tunnels, stations, airports and subways, play fundamental roles in modern society. Engineering failures of transportation infrastructures may result in significant damage to the public. The traditional methods are to monitor, store and analyze the information during the infrastructure and material design, testing, construction, numerical simulations, evaluation, operation, maintenance and preservation, using mechanistic-based, material based and statistics-based approaches. In recent decades, artificial intelligence (AI) has drawn the attention of many researchers and has been used as a powerful tool to understand and analyze the engineering failures in transportation infrastructure and materials. AI has the advantages of conveniently characterizing infrastructure materials in multiscale, extracting failure information from images and cloud points, evaluating performance from the signals of sensors, predicting the long-term performance of infrastructure based on big data and optimizing infrastructure maintenance strategies, etc

Facial Recognition and Face Mask Detection Using Machine Learning Techniques

Facial recognition, as a biometric system, is a crucial tool for the identification procedures. When using facial recognition, an individual\u27s identity is identified using their unique facial features. Biometric authentication system helps in identifying individuals using their physiological and behavioral features. Physiological biometrics utilize human features such as faces, irises, and fingerprints. In contrast, behavioral biometric rely on features that humans do, such as voice and handwritings. Facial recognition has been widely used for security and other law enforcement purposes. However, since COVID-19 pandemic, many people around the world had to wear face masks. This thesis introduces a neural network system, which can be trained to identify people’s facial features while half of their faces are covered by face masks. The Convolutional Neural Network (CNN) model using transfer learning technique has achieved remarkable accuracy even the original dataset is very limited. One large Face mask detection dataset was first used to train the model, while the original much smaller Face mask detector dataset was used to adapt and finetune this model that was previously generated. During the training and testing phases, network structures, and various parameters were adjusted to achieve the best accuracy results for the actual small dataset. Our adapted model was able to achieve a 97.1% accuracy