Tracking multiple people across multiple cameras is an open problem. It is typically divided into two tasks: (i) single-camera tracking (SCT) - identify trajectories in the same scene, and (ii) inter-camera tracking (ICT) - identify trajectories across cameras for real surveillance scenes. Many methods cater to SCT, while ICT still remains a challenge. In this paper, we present a feature aggregation architecture called Composite Appearance Network (CAN) to address the above problem. The key structure of this architecture is called EvalNet that pays attention to each feature vector and learns to weight them based on gradients it receives for the overall template for optimal re-identification performance. We demonstrate the efficiency of our approach with experiments on the challenging multi-camera tracking dataset, DukeMTMC. We also survey existing tracking measures and present an online error metric called "Inference Error" (IE) that provides a better estimate of tracking re-identification error, by treating SCT and ICT errors uniformly.
The framework proposed in this paper is inspired from @cite_21 . The authors propose set based feature aggregation network (FAN) for the face verification problem. By generating representative template features using metadata like yaw, pitch and face size, their system could outperform traditional pooling approaches. Instead of building a siamese network, we focus on learning the pooled feature for gallery template (trajectory) while coupling the probe metadata beside gallery metadata. This way, we derive each trajectory's representation by considering the impact or influence of the probe detection in the context of data association.
@cite_21
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This paper presents a novel approach to feature aggregation for template set based face recognition by incorporating metadata regarding face images to evaluate the representativeness of a feature in the template. We propose using orthogonal data like yaw, pitch, face size, etc. to augment the capacity of deep neural networks to find stronger correlations between the relative quality of the face image in the set with the match performance. The approach presented employs a siamese architecture for training on features and metadata generated using other state-of-the-art CNNs and learns an effective feature fusion strategy for producing optimal face verification performance. We obtain substantial improvements in TAR of over 1.5 at 10^-4 FAR as compared to traditional pooling approaches and illustrate the efficacy of the quality assessment made by the network on the two challenging datasets IJB-A and IARPA Janus CS4. We propose a new approach to video face recognition. Our component-wise feature aggregation network (C-FAN) accepts a set of face images of a subject as an input, and outputs a single feature vector as the face representation of the set for the recognition task. The whole network is trained in two steps: (i) train a base CNN for still image face recognition; (ii) add an aggregation module to the base network to learn the quality value for each feature component, which adaptively aggregates deep feature vectors into a single vector to represent the face in a video. C-FAN automatically learns to retain salient face features with high quality scores while suppressing features with low quality scores. The experimental results on three benchmark datasets, YouTube Faces, IJB-A, and IJB-S show that the proposed C-FAN network is capable of generating a compact feature vector with 512 dimensions for a video sequence by efficiently aggregating feature vectors of all the video frames to achieve state of the art performance. Recent years have witnessed promising results of face detection using deep learning, especially for the family of region-based convolutional neural networks (R-CNN) methods and their variants. Despite making remarkable progresses, face detection in the wild remains an open research challenge especially when detecting faces at vastly different scales and characteristics. In this paper, we propose a novel framework of "Feature Agglomeration Networks" (FAN) to build a new single stage face detector, which not only achieves state-of-the-art performance but also runs efficiently. As inspired by the recent success of Feature Pyramid Networks (FPN) lin2016fpn for generic object detection, the core idea of our framework is to exploit inherent multi-scale features of a single convolutional neural network to detect faces of varied scales and characteristics by aggregating higher-level semantic feature maps of different scales as contextual cues to augment lower-level feature maps via a hierarchical agglomeration manner at marginal extra computation cost. Unlike the existing FPN approach, we construct our FAN architecture using a new Agglomerative Connection module and further propose a Hierarchical Loss to effectively train the FAN model. We evaluate the proposed FAN detector on several public face detection benchmarks and achieved new state-of-the-art results with real-time detection speed on GPU. Much research has been conducted on both face identification and face verification, with greater focus on the latter. Research on face identification has mostly focused on using closed-set protocols, which assume that all probe images used in evaluation contain identities of subjects that are enrolled in the gallery. Real systems, however, where only a fraction of probe sample identities are enrolled in the gallery, cannot make this closed-set assumption. Instead, they must assume an open set of probe samples and be able to reject ignore those that correspond to unknown identities. In this paper, we address the widespread misconception that thresholding verification-like scores is a good way to solve the open-set face identification problem, by formulating an open-set face identification protocol and evaluating different strategies for assessing similarity. Our open-set identification protocol is based on the canonical labeled faces in the wild (LFW) dataset. Additionally to the known identities, we introduce the concepts of known unknowns (known, but uninteresting persons) and unknown unknowns (people never seen before) to the biometric community. We compare three algorithms for assessing similarity in a deep feature space under an open-set protocol: thresholded verification-like scores, linear discriminant analysis (LDA) scores, and an extreme value machine (EVM) probabilities. Our findings suggest that thresholding EVM probabilities, which are open-set by design, outperforms thresholding verification-like scores.