Person re-identification is an important task in video surveillance systems. It can be formally defined as establishing the correspondence between images of a person taken from different cameras at different times. In this paper, we present a two stream convolutional neural network where each stream is a Siamese network. This architecture can learn spatial and temporal information separately. We also propose a weighted two stream training objective function which combines the Siamese cost of the spatial and temporal streams with the objective of predicting a person's identity. We evaluate our proposed method on the publicly available PRID2011 and iLIDS-VID datasets and demonstrate the efficacy of our proposed method. On average, the top rank matching accuracy is 4% higher than the accuracy achieved by the cross-view quadratic discriminant analysis used in combination with the hierarchical Gaussian descriptor (GOG+XQDA), and 5% higher than the recurrent neural network method.

The bag of visual words (BOW) model is widely used for image representation and classification. Spatial pyramid based feature pooling utilizes the BOW model and is the most popular approach to capture the spatial distribution (layout) of local image features. It makes the assumption that the center of an object is aligned with the center of an image, which can lead to misalignment and degradation in performance. In this paper, we propose a method to utilize max pooled features to estimate objects centers and align the spatial pyramid accordingly. We also propose an image representation descriptor robust to misalignments and objects deformations. The experimental results demonstrate that our spatial pyramid alignment method is simple yet efficient in handling misalignments and achieves high object classification accuracy.

Pedestrian detection is a fundamental task for many applications including autonomous vehicles and surveillance systems. In a mobile or networked environment bandwidth is limited and adaptive datarate streaming is used. Video compression can introduce significant quality degradation that impacts the accuracy of video analytics. In this paper, we examine the problem of a changing video data-rate and examine how it affects the performance of video analytics, in particular pedestrian detection, using a two-stage quality-adaptive convolutional neural network system. Our experimental results demonstrate that when adaptive data-rate streaming is used, our proposed quality-adaptive approach reduces the miss rate by 20% compared to the baseline detector.

In this paper, we address the problem of predicting accuracy for pedestrian detection. We want to be able to predict the accuracy of a video analytic method without actually executing the method. We propose the use of texture descriptors and random forests to predict the accuracy of various pedestrian detection methods. Our experimental results demonstrate that using the local binary pattern (LBP) or a bank of Schmid and Gabor filters can capture spatial textural information associated with video quality degradation that can be used to predict accuracy. We also demonstrate how predicting the absolute accuracy can save network and computational resources.

Person re-identification is the process of recognizing a person across a network of cameras with non-overlapping fields of view. In this paper we present an unsupervised multi-shot approach based on a patch-based dynamic appearance model. We use deformable graph matching for person re-identification using histograms of color and texture as features of nodes. Each graph model spans multiple images and each node is a local patch in the shape of a rectangle. We evaluate our proposed method on publicly available PRID 2011 and iLIDS-VID databases.

In this paper, we propose a novel method for generating visually appealing two-dimensional (2D) barcodes that resemble meaningful images to human observers. The technology of 2D barcodes, currently dominated by quick response codes, is widely adopted in many applications, including product tracking, document management, and general marketing. Such barcodes typically lack user friendly appearance and do not convey any visual significance to human observers. The proposed method addresses this problem by allowing 2D barcodes to resemble an arbitrary image or a logo. Our method is based on a generalization of the Kuznetsov-Tsybakov problem that served as a foundation for wet paper codes, commonly adopted in digital steganography. We introduce weaker statistical constraints to obtain additional flexibility allowing the barcode to assume the appearance of an arbitrary pattern. This paper provides the theoretical analysis of the proposed coding framework and a practical algorithm for rapid approximation of the optimal code. We also discuss the introduction of error correction capabilities, and experimentally evaluate a prototype implementation in a smartphone-based acquisition scenario.

In the field of content based video retrieval, recent work has focused on creation and matching of signatures as an effective method for video search, copy or “near-duplicate” detection. In this paper, we address the question of how much content is enough to initiate a content based video retrieval query from a mobile device. We extract and utilize the motion vectors from an HEVC-encoded bitstream and use them as the content-dependent features to create video signatures. We propose an energy function to quantify the mount of motion information contained in video. Our experimental results exhibit that our proposed approach is able to generate a signature robust to common signal processing techniques and that our proposed energy function can be efficiently used to select the length of a video clip used to query the video database from a mobile device.

Entropy coding is an integral part of most data compression systems. Huffman coding (HC) and arithmetic coding (AC) are two of the most widely used coding methods. HC can process a large symbol alphabet at each step allowing for fast encoding and decoding. However, HC typically provides suboptimal data rates due to its inherent approximation of symbol probabilities to powers of 1 over 2. In contrast, AC uses nearly accurate symbol probabilities, hence generally providing better compression ratios. However, AC relies on relatively slow arithmetic operations making the implementation computationally demanding. In this paper we discuss asymmetric numeral systems (ANS) as a new approach to entropy coding. While maintaining theoretical connections with AC, the proposed ANS-based coding can be implemented with much less computational complexity. While AC operates on a state defined by two numbers specifying a range, an ANS-based coder operates on a state defined by a single natural number such that the x ∈ ℕ state contains ≈ log2(x) bits of information. This property allows to have the entire behavior for a large alphabet summarized in the form of a relatively small table (e.g. a few kilobytes for a 256 size alphabet). The proposed approach can be interpreted as an equivalent to adding fractional bits to a Huffman coder to combine the speed of HC and the accuracy offered by AC. Additionally, ANS can simultaneously encrypt a message encoded this way. Experimental results demonstrate effectiveness of the proposed entropy coder.

Many two-dimensional (2D) barcodes, such as quick response (QR) codes, lack user-friendly appearance. Our goal in this paper is to generate 2D barcodes that “look” like recognizable images or logos. Standard steganographic methods hide a message (payload) in an image usually by modifying bits in a specific way using predetermined pixels of the image. This approach cannot be directly used for very low bit rates commonly used in 1 bit per pixel 2D barcodes. It is possible to produce barcodes in which the grayness of a pixel in an image is interpreted as the probability of assigning a value (black or white) to the corresponding pixel of the encoded message (payload). This can be viewed as statistical constraints enforced on the encoded bit-sequence. Using an information theoretic approach, Kuznetsov and Tsybakov have shown that this can be done for a specific case of constraints almost without any loss of capacity. In this paper, we propose generalizations of this approach with weaker constraints as an application to generating 2D barcodes that resemble images. We describe a coding framework, various types of constraints, a practical approximation and some example 2D barcodes generated from our implementation.