Csaba Beleznai

Person re-identification by descriptive and discriminative classification

Person re-identification, i.e., recognizing a single person across spatially disjoint cameras, is an important task in visual surveillance. Existing approaches either try to find a suitable description of the appearance or learn a discriminative model. Since these different representational strategies capture a large extent of complementary information we propose to combine both approaches. First, given a specific query, we rank all samples according to a feature-based similarity, where appearance is modeled by a set of region covariance descriptors. Next, a discriminative model is learned using boosting for feature selection, which provides a more specific classifier. The proposed approach is demonstrated on two datasets, where we show that the combination of a generic descriptive statistical model and a discriminatively learned feature-based model attains considerably better results than the individual models alone. In addition, we give a comparison to the state-of-the-art on a publicly available benchmark dataset.

Mahalanobis Distance Learning for Person Re-identification

Recently, Mahalanobis metric learning has gained a considerable interest for single-shot person re-identification. The main idea is to build on an existing image representation and to learn a metric that reflects the visual camera-to-camera transitions, allowing for a more powerful classification. The goal of this chapter is twofold. We first review the main ideas of Mahalanobis metric learning in general and then give a detailed study on different approaches for the task of single-shot person re-identification, also comparing to the state of the art. In particular, for our experiments, we used Linear Discriminant Metric Learning (LDML), Information Theoretic Metric Learning (ITML), Large Margin Nearest Neighbor (LMNN), Large Margin Nearest Neighbor with Rejection (LMNN-R), Efficient Impostor-based Metric Learning (EIML), and KISSME. For our evaluations we used four different publicly available datasets (i.e., VIPeR, ETHZ, PRID 2011, and CAVIAR4REID). Additionally, we generated the new, more realistic PRID 450S dataset, where we also provide detailed segmentations. For the latter one, we also evaluated the influence of using well-segmented foreground and background regions. Finally, the corresponding results are presented and discussed.

Fast human detection in crowded scenes by contour integration and local shape estimation

The complexity of human detection increases significantly with a growing density of humans populating a scene. This paper presents a Bayesian detection framework using shape and motion cues to obtain a maximum a posteriori (MAP) solution for human configurations consisting of many, possibly occluded pedestrians viewed by a stationary camera. The paper contains two novel contributions for the human detection task: 1. computationally efficient detection based on shape templates using contour integration by means of integral images which are built by oriented string scans; (2) a non-parametric approach using an approximated version of the shape context descriptor which generates informative object parts and infers the presence of humans despite occlusions. The outputs of the two detectors are used to generate a spatial configuration of hypothesized human body locations. The configuration is iteratively optimized while taking into account the depth ordering and occlusion status of the hypotheses. The method achieves fast computation times even in complex scenarios with a high density of people. Its validity is demonstrated on a substantial amount of image data using the CAVIAR and our own datasets. Evaluation results and comparison with state of the art are presented.

Performance evaluation metrics for motion detection and tracking

This work introduces a methodology for evaluating the operational range of a video surveillance system in terms of robustness and reliability. We propose the generation of semi and full-synthetic video sequences under controlled variation of selected parameters. This data provides the necessary ground truth information for evaluating the motion detection and tracking systems. In addition, we propose several error metrics for quantitative evaluation.

Human Tracking by Fast Mean Shift Mode Seeking

Change detection by background subtraction is a common approach to detect moving foreground. The resulting difference image is usually thresholded to obtain objects based on pixel connectedness and resulting blob objects are subsequently tracked. This paper proposes a detection approach not requiring the binarization of the difference image. Local density maxima in the difference image - usually representing moving objects - are outlined by a fast non-parametric mean shift clustering procedure. Object tracking is carried out by updating and propagating cluster parameters over time using the mode seeking property of the mean shift procedure. For occluding targets, a fast procedure determining the object configuration maximizing image likelihood is presented. Detection and tracking results are demonstrated for a crowded scene and evaluation of the proposed tracking framework is presented.

Human detection in groups using a fast mean shift procedure

Detecting individual humans within groups becomes a non-trivial task when performing automatic visual surveillance in crowded scenes. This paper proposes a novel way to detect individual humans directly from the difference image using a fast variant of the mean shift mode seeking procedure and verifying the hypothesized configuration by a model-based approach. The method runs in real-time. Promising result are demonstrated for challenging image sequences.

Human tracking by mode seeking

Change detection by background subtraction is a common approach to detect moving foreground. The resulting difference image is usually thresholded to obtain objects based on pixel connectedness and resulting blob objects are subsequently tracked. This paper proposes a detection approach not requiring the binarization of the difference image. Local density maxima in the difference image - usually representing moving objects - are outlined by a fast non-parametric mean shift clustering procedure. Object tracking is carried out by updating and propagating cluster parameters over time using the mode seeking property of the mean shift procedure. For occluding targets, a fast procedure determining the object configuration maximizing image likelihood is presented. Detection and tracking results are demonstrated for a crowded scene and evaluation of the proposed tracking framework is presented.

Dense appearance modeling and efficient learning of camera transitions for person re-identification

One central task in many visual surveillance scenarios is person re-identification, i.e., recognizing an individual person across a network of spatially disjoint cameras. Most successful recognition approaches are either based on direct modeling of the human appearance or on machine learning. In this work, we aim at taking advantage of both directions of research. On the one hand side, we compute a descriptive appearance representation encoding the vertical color structure of pedestrians. To improve the classification results, we additionally estimate the transition between two cameras using a pair-wisely estimated metric. In particular, we introduce 4D spatial color histograms and adopt Large Margin Nearest Neighbor (LMNN) metric learning. The approach is demonstrated for two publicly available datasets, showing competitive results, however, on lower computational costs.

Multiple Object Tracking Using Local PCA

Tracking multiple interacting objects represents a challenging area in computer vision. The tracking problem in general can be formulated as the task of recovering the spatio-temporal trajectories for an unknown number of objects appearing and disappearing at arbitrary times. Observations are noisy, their origin is unknown, generated by true detections or false alarms. Data association and the estimation of object states are two crucial tasks to be solved in this context. This work describes a novel, computationally efficient tracking approach to generate consistent trajectories. First, trajectory segments are created by analyzing the spatio-temporal data distribution using local principal component analysis. Subsequently, linking between trajectory segments is carried out relying on spatial proximity and kinematic smoothness constraints. Tracking results are demonstrated in the context of human tracking and compared to results of a frame-to-frame-based tracking approach

Shape-based detection of humans for video surveillance applications

In this paper we describe a surveillance system that is not only able to detect blobs and track them but also determines if a blob is a person. The given blob is segmented into sub-regions. A person model is fit to these regions such that a likelihood measure is maximized. The likelihood measure depends on the number of identified body parts, their length, location, and aspect ratio. The method is translation, rotation, and scale invariant and computationally efficient. The results obtained for test video sequences are very encouraging.