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Databases for Multi-camera , Network Camera , E-Surveillace

Posted by Hemprasad Y. Badgujar on February 18, 2016

Multi-view, Multi-Class Dataset: pedestrians, cars and buses

This dataset consists of 23 minutes and 57 seconds of synchronized frames taken at 25fps from 6 different calibrated DV cameras.
One camera was placed about 2m high of the ground, two others where located on a first floor high, and the rest on a second floor to cover an area of 22m x 22m.
The sequence was recorded at the EPFL university campus where there is a road with a bus stop, parking slots for cars and a pedestrian crossing.


Ground truth images
Ground truth annotations


The dataset on this page has been used for our multiview object pose estimation algorithm described in the following paper:

G. Roig, X. Boix, H. Ben Shitrit and P. Fua Conditional Random Fields for Multi-Camera Object Detection, ICCV11.

Multi-camera pedestrians video

“EPFL” data set: Multi-camera Pedestrian Videos

people tracking
results, please cite one of the references below.

On this page you can download a few multi-camera sequences that we acquired for developing and testing our people detection and tracking framework. All of the sequences feature several synchronised video streams filming the same area under different angles. All cameras are located about 2 meters from the ground. All pedestrians on the sequences are members of our laboratory, so there is no privacy issue. For the Basketball sequence, we received consent from the team.

Laboratory sequences

These sequences were shot inside our laboratory by 4 cameras. Four (respectively six) people are sequentially entering the room and walking around for 2 1/2 minutes. The frame rate is 25 fps and the videos are encoded using MPEG-4 codec.

[Camera 0] [Camera 1] [Camera 2] [Camera 3]

Calibration file for the 4 people indoor sequence.

[Camera 0] [Camera 1] [Camera 2] [Camera 3]

Calibration file for the 6 people indoor sequence.

Campus sequences

These two sequences called campus were shot outside on our campus with 3 DV cameras. Up to four people are simultaneously walking in front of them. The white line on the screenshots shows the limits of the area that we defined to obtain our tracking results. The frame rate is 25 fps and the videos are encoded using Indeo 5 codec.

[Seq.1, cam. 0] [Seq.1, cam. 1] [Seq.1, cam. 2]
[Seq.2, cam. 0] [Seq.2, cam. 1] [Seq.2, cam. 2]

Calibration file for the two above outdoor scenes.

Terrace sequences

The sequences below, called terrace, were shot outside our building on a terrace. Up to 7 people evolve in front of 4 DV cameras, for around 3 1/2 minutes. The frame rate is 25 fps and the videos are encoded using Indeo 5 codec.

[Seq.1, cam. 0] [Seq.1, cam. 1] [Seq.1, cam. 2] [Seq.1, cam. 3]
[Seq.2, cam. 0] [Seq.2, cam. 1] [Seq.2, cam. 2] [Seq.1, cam. 3]

Calibration file for the terrace scene.

Passageway sequence

This sequence dubbed passageway was filmed in an underground passageway to a train station. It was acquired with 4 DV cameras at 25 fps, and is encoded with Indeo 5. It is a rather difficult sequence due to the poor lighting.

[Seq.1, cam. 0] [Seq.1, cam. 1] [Seq.1, cam. 2] [Seq.1, cam. 3]

Calibration file for the passageway scene.

Basketball sequence

This sequence was filmed at a training session of a local basketball team. It was acquired with 4 DV cameras at 25 fps, and is encoded with Indeo 5.

[Seq.1, cam. 0] [Seq.1, cam. 1] [Seq.1, cam. 2] [Seq.1, cam. 3]

Calibration file for the basketball scene.

Camera calibration

POM only needs a simple calibration consisting of two homographies per camera view, which project the ground plane in top view to the ground plane in camera views and to the head plane in camera views (a plane parallel to the ground plane but located 1.75 m higher). Therefore, the calibration files given above consist of 2 homographies per camera. In degenerate cases where the camera is located inside the head plane, this one will project to a horizontal line in the camera image. When this happens, we do not provide a homography for the head plane, but instead we give the height of the line in which the head plane will project. This is expressed in percentage of the image height, starting from the top.

The homographies given in the calibration files project points in the camera views to their corresponding location on the top view of the ground plane, that is

H * X_image = X_topview .

We have also computed the camera calibration using the Tsai calibration toolkit for some of our sequences. We also make them available for download. They consist of an XML file per camera view, containing the standard Tsai calibration parameters. Note that the image size used for calibration might differ from the size of the video sequences. In this case, the image coordinates obtained with the calibration should be normalized to the size of the video.

Ground truth

We have created a ground truth data for some of the video sequences presented above, by locating and identifying the people in some frames at a regular interval.

To use these ground truth files, you must rely on the same calibration with the exact same parameters that we used when generating the data. We call top view the rectangular area of the ground plane in which we perform tracking.

This area is of dimensions tv_width x tv_height and has top left coordinate (tv_origin_x, tv_origin_y). Besides, we call grid our discretization of the top view area into grid_width x grid_height cells. An example is illustrated by the figure below, in which the grid has dimensions 5 x 4.

The people’s position in the ground truth are expressed in discrete grid coordinates. In order to be projected into the images with homographies or the Tsai calibration, these grid coordinates need to be translated into top view coordinates. We provide below a simple C function that performs this translation. This function takes the following parameters:

  • pos : the person position coming from the ground truth file
  • grid_width, grid_height : the grid dimension
  • tv_origin_x, tv_origin_y : the top left corner of the top view
  • tv_width, tv_height : the top view dimension
  • tv_x, tv_y : the top view coordinates, i.e. the output of the function
  void grid_to_tv(int pos, int grid_width, int grid_height,                  float tv_origin_x, float tv_origin_y, float tv_width,                  float tv_height, float &tv_x, float &tv_y) {     tv_x = ( (pos % grid_width) + 0.5 ) * (tv_width / grid_width) + tv_origin_x;    tv_y = ( (pos / grid_width) + 0.5 ) * (tv_height / grid_height) + tv_origin_y;  }

The table below summarizes the aforementionned parameters for the ground truth files we provide. Note that the ground truth for the terrace sequence has been generated with the Tsai calibration provided in the table. You will need to use this one to get a proper bounding box alignment.

Ground Truth Grid dimensions Top view origin Top view dimensions Calibration
6-people laboratory 56 x 56 (0 , 0) 358 x 360 file
terrace, seq. 1 30 x 44 (-500 , -1,500) 7,500 x 11,000 file (Tsai)
passageway, seq. 1 40 x 99 (0 , 38.48) 155 x 381 file

The format of the ground truth file is the following:

 1 <number of frames>  <number of people>  <grid width>  <grid height>  <step size>  <first frame>  <last frame> <pos> <pos> <pos> ... <pos> <pos> <pos> ... . . .

where <number of frames> is the total number of frames, <number of people> is the number of people for which we have produced a ground truth, <grid width> and <grid height>are the ground plane grid dimensions, <step size> is the frame interval between two ground truth labels (i.e. if set to 25, then there is a label once every 25 frames), and <first frame> and <last frame> are the first and last frames for which a label has been entered.

After the header, every line represents the positions of people at a given frame. <pos> is the position of a person in the grid. It is normally a integer >= 0, but can be -1 if undefined (i.e. no label has been produced for this frame) or -2 if the person is currently out of the grid.


Multiple Object Tracking using K-Shortest Paths Optimization

Jérôme Berclaz, François Fleuret, Engin Türetken, Pascal Fua
IEEE Transactions on Pattern Analysis and Machine Intelligence
pdf | show bibtex

Multi-Camera People Tracking with a Probabilistic Occupancy Map

François Fleuret, Jérôme Berclaz, Richard Lengagne, Pascal Fua
IEEE Transactions on Pattern Analysis and Machine Intelligence
pdf | show bibtex

MuHAVi: Multicamera Human Action Video Data

including selected action sequences with

MAS: Manually Annotated Silhouette Data

for the evaluation of human action recognition methods

Figure 1. The top view of the configuration of 8 cameras used to capture the actions in the blue action zone (which is marked with white tapes on the scene floor).

camera symbol

camera name

V1 Camera_1
V2 Camera_2
V3 Camera_3
V4 Camera_4
V5 Camera_5
V6 Camera_6
V7 Camera_7
V8 Camera_8

Table 1. Camera view names appearing in the MuHAVi data folders and the corresponding symbols used in Fig. 1.


On the table below, you can click on the links to download the data (JPG images) for the corresponding action

Important: We noted that some earlier versions of that earlier versions of MS Internet Explorer could not download files over 2GB size, so we recomment to use alternative browsers such as Firefox or Chrome.

Each tar file contains 7 folders corresponding to 7 actors (Person1 to Person7) each of which contains 8 folders corresponding to 8 cameras (Camera_1 to Camera_8). Image frames corresponding to every combination of action/actor/camera are named with image frame numbers starting from 00000001.jpg for simplicity. The video frame rate is 25 frames per second and the resolution of image frames (except for Camera_8) is 720 x 576 Pixels (columns x rows). The image resolution is 704 x 576 for Camera_8.

action class

action name

C1 WalkTurnBack 2.6GB
C2 RunStop 2.5GB
C3 Punch 3.0GB
C4 Kick 3.4GB
C5 ShotGunCollapse 4.3GB
C6 PullHeavyObject 4.5GB
C7 PickupThrowObject 3.0GB
C8 WalkFall 3.9GB
C9 LookInCar 4.6GB
C10 CrawlOnKnees 3.4GB
C11 WaveArms 2.2GB
C12 DrawGraffiti 2.7GB
C13 JumpOverFence 4.4GB
C14 DrunkWalk 4.0GB
C15 ClimbLadder 2.1GB
C16 SmashObject 3.3GB
C17 JumpOverGap 2.6GB

MIT Trajectory Data Set – Multiple Camera Views


MIT trajectory data set is for the research of activity analysis in multiple single camera view using the trajectories of objects as features. Object tracking is based on background subtraction using a Adaptive Gaussian Mixture model. There are totally four camera views. Trajectories in different camera views have been synchronized. The data can be downloaded from the following link,

MIT trajectory data set

Background image


Please cite as:

X. Wang, K. Tieu and E. Grimson, Correspondence‐Free Activity Analysis and Scene Modeling in Multiple Camera Views, IEEE Transactions on Pattern Analysis and Machine Intelligence(PAMI), Vol. 32, pp. 56-71, 2010..


MIT traffic data set is for research on activity analysis and crowded scenes. It includes a traffic video sequence of 90 minutes long. It is recorded by a stationary camera. The size of the scene is 720 by 480. It is divided into 20 clips and can be downloaded from the following links.

Ground Truth

In order to evaluate the performance of human detection on this data set, ground truth of pedestrians of some sampled frames are manually labeled. It can be downloaded below. A readme file provides the instructions of how to use it.
Ground truth of pedestrians


  1. Unsupervised Activity Perception in Crowded and Complicated scenes Using Hierarchical Bayesian Models
    X. Wang, X. Ma and E. Grimson
    IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol. 31, pp. 539-555, 2009
  2. Automatic Adaptation of a Generic Pedestrian Detector to a Specific Traffic Scene
    M. Wang and X. Wang
    IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2011


This dataset is presented in our CVPR 2015 paper,
Linjie Yang, Ping Luo, Chen Change Loy, Xiaoou Tang. A Large-Scale Car Dataset for Fine-Grained Categorization and Verification, In Computer Vision and Pattern Recognition (CVPR), 2015. PDF

The Comprehensive Cars (CompCars) dataset contains data from two scenarios, including images from web-nature and surveillance-nature. The web-nature data contains 163 car makes with 1,716 car models. There are a total of 136,726 images capturing the entire cars and 27,618 images capturing the car parts. The full car images are labeled with bounding boxes and viewpoints. Each car model is labeled with five attributes, including maximum speed, displacement, number of doors, number of seats, and type of car. The surveillance-nature data contains 50,000 car images captured in the front view. Please refer to our paper for the details.

The dataset is well prepared for the following computer vision tasks:

  • Fine-grained classification
  • Attribute prediction
  • Car model verification

The train/test subsets of these tasks introduced in our paper are included in the dataset. Researchers are also welcome to utilize it for any other tasks such as image ranking, multi-task learning, and 3D reconstruction.


  1. You need to complete the release agreement form to download the dataset. Please see below.
  2. The CompCars database is available for non-commercial research purposes only.
  3. All images of the CompCars database are obtained from the Internet which are not property of MMLAB, The Chinese University of Hong Kong. The MMLAB is not responsible for the content nor the meaning of these images.
  4. You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purposes, any portion of the images and any portion of derived data.
  5. You agree not to further copy, publish or distribute any portion of the CompCars database. Except, for internal use at a single site within the same organization it is allowed to make copies of the database.
  6. The MMLAB reserves the right to terminate your access to the database at any time.
  7. All submitted papers or any publicly available text using the CompCars database must cite the following paper:
    Linjie Yang, Ping Luo, Chen Change Loy, Xiaoou Tang. A Large-Scale Car Dataset for Fine-Grained Categorization and Verification, In Computer Vision and Pattern Recognition (CVPR), 2015.

Download instructions

Download the CompCars dataset Release Agreement, read it carefully, and complete it appropriately. Note that the agreement should be signed by a full-time staff member (that is, student is not acceptable). Then, please scan the signed agreement and send it to Mr. Linjie Yang (yl012(at) and cc to Chen Change Loy (ccloy(at) We will verify your request and contact you on how to download the database.

Stanford Cars Dataset


       The Cars dataset contains 16,185 images of 196 classes of cars. The data is split into 8,144 training images and 8,041 testing images, where each class has been split roughly in a 50-50 split. Classes are typically at the level of Make, Model, Year, e.g. 2012 Tesla Model S or 2012 BMW M3 coupe.


       Training images can be downloaded here.
Testing images can be downloaded here.
A devkit, including class labels for training images and bounding boxes for all images, can be downloaded here.
If you’re interested in the BMW-10 dataset, you can get that here.

Update: For ease of development, a tar of all images is available here and all bounding boxes and labels for both training and test are available here. If you were using the evaluation server before (which is still running), you can use test annotations here to evaluate yourself without using the server.


       An evaluation server has been set up here. Instructions for the submission format are included in the devkit. This dataset was featured as part of FGComp 2013, and competition results are directly comparable to results obtained from evaluating on images here.


       If you use this dataset, please cite the following paper:

3D Object Representations for Fine-Grained Categorization
Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei
4th IEEE Workshop on 3D Representation and Recognition, at ICCV 2013 (3dRR-13). Sydney, Australia. Dec. 8, 2013.
[pdf]   [BibTex]   [slides]

Note that the dataset, as released, has 196 categories, one less than in the paper, as it has been cleaned up slightly since publication. Numbers should be more or less comparable, though.

The HDA dataset is a multi-camera high-resolution image sequence dataset for research on high-definition surveillance. 18 cameras (including VGA, HD and Full HD resolution) were recorded simultaneously during 30 minutes in a typical indoor office scenario at a busy hour (lunch time) involving more than 80 persons. In the current release (v1.1), 13 cameras have been fully labeled.


The venue spans three floors of the Institute for Systems and Robotics (ISR-Lisbon) facilities. The following pictures show the placement of the cameras. The 18 recorded cameras are identified with a small red circle. The 13 cameras with a coloured view field have been fully labeled in the current release (v1.1).


Each frame is labeled with the bounding boxes tightly adjusted to the visible body of the persons, the unique identification of each person, and flag bits indicating occlusion and crowd:

  • The bounding box is drawn so that it completely and tightly encloses the person.
  • If the person is occluded by something (except image boundaries), the bounding box is drawn by estimating the whole body extent.
  • People partially outside the image boundaries have their BB’s cropped to image limits. Partially occluded people and people partially outside the image boundaries are marked as ‘occluded’.
  • A unique ID is associated to each person, e.g., ‘person01’. In case of identity doubt, the special ID ‘personUnk’ is used.
  • Groups of people that are impossible to label individually are labelled collectively as ‘crowd’. People in front of a ’crowd’ area are labeled normally.

The following figures show examples of labeled frames: (a) an unoccluded person; (b) two occluded people; (c) a crowd with three people in front.


Data formats:

For each camera we provide the .jpg frames sequentially numbered and a .txt file containing the annotations according to the “video bounding box” (vbb) format defined in the Caltech Pedestrian Detection Database. Also on this site there are tools to visualise the annotations overlapped on the image frames.


Some statistics:

Labeled Sequences: 13

Number of Frames: 75207

Number of Bounding Boxes: 64028

Number of Persons: 85


Repository of Results:

We maintain a public repository of re-identification results in this dataset. Send us your CMC curve to be uploaded  (alex at isr ist utl pt).
Click here to see the full list and detailed experiments.

MANUAL_c_l_e_a_n cam60


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