• SentiVeillance SDK

 

SENTIVEILLANCE SDK
PERSONS OR VEHICLES RECOGNITION AND TRACKING FOR VIDEO SURVEILLANCE SYSTEMS
SentiVeillance SDK is designed for developing software that performs biometric face identification, detects moving pedestrians or vehicles or other objects and performs automatic license plate recognition using live video streams from digital surveillance cameras.
The SDK is used for passive identification – when passers-by do not make any efforts to be recognized. List of possible uses includes law enforcement, security, attendance control, visitor counting, traffic monitoring and other commercial applications.

SENTIVEILLANCE SDK
视频监控系统中的人员或车辆识别与跟踪技术
SentiVeillance SDK是为开发执行以下操作的软件而设计的:生物识别人脸识别,检测移动的行人或车辆或其他对象并执行自动车牌识别,它使用来自数码监控摄像头的现场录像数据流。
SDK适用于被动识别——当路人不愿意配合验证的场景。可能的用途包括执法、安全、出勤控制、访客计数、交通监测和其他商业应用。

 

FEATURES AND CAPABILITIES

  • Real time pedestrians and vehicles tracking and classification.
  • Biometric facial identification and matching against watchlist database.
  • Automated license plate recognition (ALPR) for moving vehicles.
  • Color, size and movement vector estimation for vehicles and other objects.
  • Gender classification, age evaluation, facial expression and attributes detection.
  • Automatic operation logs and reports events, adds new faces from video stream to watchlist.
  • Large surveillance systems support with multiple cameras.
  • Ready-to-use server for integration into video management systems (VMS) optionally available.
  • Reasonable prices, flexible licensing and free customer support.

产品特点和功能

  • 实时行人和车辆的跟踪和分类。
  • 生物识别面部识别和与特定人群(黑名单/白名单)数据库的匹配。
  • 自动车牌识别(ALPR)用于移动车辆。
  • 车辆和其他物体的颜色、大小和运动矢量估计。
  • 性别分类,年龄评估,面部表情和属性检测。
  • 自动操作日志和事件报告,添加从视频流到监视列表的新面孔。
  • 支持多个摄像头的大型监视系统.
  • 现成服务器可选地集成到视频管理系统(VMS)中。
  • 合理的价格,灵活的许可和免费的客户支持。

 

The SentiVeillance 7.0 technology has these specific capabilities:

  • Real time performance. SentiVeillance technology performs face recognition, pedestrian or vehicle classification and tracking in real time. The technology is designed to run on multi-core processors to achieve fast performance.
  • Three modalities for surveillance systems. Depending on the surveillance system design, one of these modalities may be used:
    • Biometric face recognition – based on deep neural networks and provides these capabilities for surveillance systems:
      • Multiple face detection, features extraction and template matching with the internal database in real time.
      • Facial identification reliability enables using large watchlist databases.
      • Face tracking is performed in all successive frames from the video source until they disappear from camera field of view. The face tracking algorithm uses dynamic face and motion prediction models that make it robust to occlusions like other objects or even other faces. The algorithm is able to continue tracking a face even when it re-appears after being fully covered by occlusions (like walls, furniture, posters etc).
      • Gender classification (optional) for each person in the frame.
      • Age determination (optional) for each person in the frame.
      • Smileopen-mouthclosed-eyesglassesdark-glassesbeard and mustache attributes detection (configurable).
    • Vehicle or human detection, classification and movement tracking – performs object detection of moving and static objects in the scene, their classification and tracking until they disappear. These features are available for surveillance systems:
      • Object classification. SentiVeillance allows to perform object classification, locations and tracking based on its type. Currently these classes are available: Person, Car, Bus, Truck, Bike.
      • Color estimation. The algorithm returns most likely color estimation for cars and pedestrians. The estimated color values are: red, orange, yellow, green, blue, silver, white, black, brown, grey.
      • Movement vector estimation. The algorithm returns movement vector estimation values like: "north", "south", "south-east" etc.
      • Tolerance to object visibility. The detection algorithm works with partially visible objects and from great distance.
    • Automated license plate recognition (ALPR) – once a vehicle has been detected, SentiVeillance ALPR algorithm detects and reads the license plate:
      • Traffic data processing. SentiVeillance algorithms can simultaneously read vehicle license plates from multiple moving vehicles.
      • Tolerance to camera position. Depending on camera resolution, the ALPR algorithm can read license plates from longer distance and higher angle.
      • Preventing cheating with replaced license plates. Integrators can use vehicle recognition and ALPR modalities together for making software logic which checks if recognized license plate corresponds other registration data, like vehicle color or type, and not being spoofed or moved from another vehicle.
  • Automatic operation. A system based on SentiVeillance 7.0 SDK is able to log on the fly all events. It can be configured to automatically report events like match with a watch list, or perform automatic enroll from video.
  • Large surveillance systems support. SentiVeillance 7.0 SDK allows to integrate its technology into surveillance systems with multiple cameras. A common PC with a GPU can process multiple video streams simultaneously.
  • Video files processing. SentiVeillance also accepts data from video files. The video files can be processed in real time as coming from a virtual camera or can be processed at maximum speed depending on hardware resources available.

SentiVeillance 7.0技术具有以下特定功能:

  • 实时性能监控技术实现人脸识别、行人或车辆分类和实时跟踪。该技术被设计为在多核处理器上运行,以实现快速性能。
  • 监视系统的三种模式。视监测系统的设计而定,可采用下列其中一种模式:
    • 生物特征人脸识别-以深层神经网络为基础,为监测系统提供这些能力:
      • 多面中的内部数据库进行检测、特征提取和模板匹配。实时.
      • 面部识别可靠性启用大型特定人群数据库。
      • 人脸跟踪在视频源的所有连续帧中执行,直到它们从摄像机视野中消失为止。人脸跟踪算法采用动态人脸和运动预测模型,使其对其他对象甚至其他人脸的遮挡具有较强的鲁棒性。该算法能够继续跟踪一张脸,即使它在完全被遮挡(如墙壁、家具、海报等)覆盖后再次出现。
      • 性别对帧中的每个人进行分类(可选)。
      • 年龄为帧中的每个人确定(可选)。
      • 微笑张嘴闭上眼睛眼镜墨镜胡须等属性检测(可配置)
    • 车辆或人体检测、分类和运动跟踪-对场景中的运动和静态物体进行目标检测,分类和跟踪,直到它们消失为止。这些功能可用于监视系统:
      • 对象分类SentiVeillance允许根据对象类型执行对象分类、位置和跟踪。目前,这些分类有:人,车,公共汽车,卡车,自行车。
      • 颜色估计该算法最有可能对汽车和行人进行颜色估计。估计颜色价值有:红色,橙色,黄色,绿色,蓝色,银色,白色,黑色,棕色,灰色。
      • 运动矢量估计该算法返回“北”、“南”、“东南”等运动矢量估计值。
      • 对目标可见性的容忍度。该检测算法适用于部分可见对象,距离较远。
    • 自动车牌识别-一旦车辆被发现,SentiVeillance ALPR算法检测并读取车牌:
      • 交通数据处理SentiVeillance Algorithms算法可以同时从多辆移动车辆中读取车辆牌照。
      • 对摄像机位置的容忍度。根据摄像机分辨率,ALPR算法可以从更远的距离和更高的角度读取车牌。
      • 用更换的车牌防止作弊。集成商可以使用车辆识别和ALPR模式共同制作软件逻辑,检查识别的车牌是否对应其他登记数据,如车辆颜色或类型,而不被欺骗或从其他车辆移动。
  • 自动操作。一个基于SentiVeillance 7.0 SDK的系统能够飞行登录所有事件。它可以自动配置为报告事件,如与监视表,或自动执行报名从录像。
  • 大型监视系统支持。SentiVeillance 7.0 SDK允许将其技术集成到监视系统中多摄像头。带有GPU的普通PC机可以同时处理多个视频流。
  • 视频文件处理。SentiVeillance还接受视频文件中的数据。视频文件可以作为来自虚拟相机的实时处理,也可以根据可用的硬件资源以******速度处理。