Person re-identification by symmetry-driven accumulation of local features


In this paper, we present an appearance-based method for person re-identification. It consists in the extraction of features that model three complementary aspects of the human appearance: the overall chromatic content, the spatial arrangement of colors into stable regions, and the presence of recurrent local motifs with high entropy. All this information is derived from different body parts, and weighted opportunely by exploiting symmetry and asymmetry perceptual principles. In this way, robustness against very low resolution, occlusions and pose, viewpoint and illumination changes is achieved. The approach applies to situations where the number of candidates varies continuously, considering single images or bunch of frames for each individual. It has been tested on several public benchmark datasets (ViPER, iLIDS, ETHZ), gaining new state-of-the-art performances.

Download full-text


Available from: Loris Bazzani, Oct 13, 2015
143 Reads
  • Source
    • "Specifically, in person Re-ID benchmarks the head is always located at the top of images, torso in the middle, and legs at the bottom. This typical structure has been exploited in designing discriminative features [10] "
    [Show abstract] [Hide abstract]
    ABSTRACT: The group membership prediction (GMP) problem involves predicting whether or not a collection of instances share a certain semantic property. For instance, in kinship verification given a collection of images, the goal is to predict whether or not they share a {\it familial} relationship. In this context we propose a novel probability model and introduce latent {\em view-specific} and {\em view-shared} random variables to jointly account for the view-specific appearance and cross-view similarities among data instances. Our model posits that data from each view is independent conditioned on the shared variables. This postulate leads to a parametric probability model that decomposes group membership likelihood into a tensor product of data-independent parameters and data-dependent factors. We propose learning the data-independent parameters in a discriminative way with bilinear classifiers, and test our prediction algorithm on challenging visual recognition tasks such as multi-camera person re-identification and kinship verification. On most benchmark datasets, our method can significantly outperform the current state-of-the-art.
  • Source
    • "Appearance of a person is the visible foreground image after background subtraction [2]. Similarly, appearance-based methods rely on clothes, visual parts or perceptual principles to extract features for object recognition [2] [3]. Texture on the other hand, contains structural arrangement of a surface and its relation with the environments [4]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents an automatic, machine vision based, military personnel identification and classification system. Classification was done using a Support Vector Machine (SVM) on sets of Army, Air Force and Navy camouflage uniform personnel datasets retrieved from google images and selected military websites. In the proposed system, the arm of service of personnel is recognised by the camouflage and the type of cap badge/logo of a persons uniform. The detailed analysis done include; camouflage and plain cap differentiation using Gray Level Co- occurrence Matrix (GLCM) texture features; Army, Air Force and Navy camouflaged uniforms differentiation using GLCM texture and colour histogram bin features; plain cap differentiation using Speed Up Robust Feature (SURF) on the cap badge. Correlation-based Feature Selection (CFS) was used to improve recognition by selecting discriminating features, thereby speeding the classification process. With this method success rates recorded during the analysis include 94% for camouflage appearance category, 100%, 90% and 100% rates of plain and camouflage cap categories for Army, Air Force and Navy respectively. Similarly, using SURF features on the cap badge in the top region of the segmented human part of top and bottom; the plain cap badge of the military personnel was accurately categorised. By this, we have shown that the proposed method can be integrated into a face recognition system, which recognises an individual and determine the arm of service the person belongs. Such a system can be used to enhance the security of a military base or facility. Substantial analysis has been carried out and results after comparison with two other techniques prove that the proposed method can correctly classify military personnel into various arms of service. Accurate recognition was recorded with the proposed technique.
  • Source
    • "One set was used for training and other for testing. We do this following the testing conventions in these papers [7], [4], [13]. "
    [Show description] [Hide description]
    DESCRIPTION: Person re-identification is to associate people across different camera views at different locations and time. Current computer vision algorithms on person re-identification mainly fo- cus on performance, making it unsuitable for distributed systems. For a distributed system, computational complexity, network usage, energy consumption and memory requirement are as im- portant as the performance. In this paper, we compare the merits of current algorithms. We consider three key algorithms, Keep It Simple and Straightforward MEtric (KISSME), Symmetry- Driven Accumulation of Local Features (SDALF) and Unsu- pervised Saliency Matching (USM). The advantage of SDALF, and USM is that they are unsupervised methods so training is not required but computationally many time expensive than KISSME. The Saliency based method is superior in performance but also has the largest feature size. As the features needs to be transmitted from one camera to other in distributed system, this mean higher energy consumption and longer time delay. Among these three, KISSME offers a balance between performance, complexity and feature lengths and hence more suitable for distributed systems.
Show more