As the year kicks off, we start this new series for installers with one of the technology leaders in this field – ioimage. And where better for the company’s marketing manager Daniel Doron to start, than at the beginning …
Video motion detection (VMD) was first implemented more than four decades ago as an analogue system, but failed to deliver due to high false alarm rates and product and installation costs.
The evolution of electronics, PCs and the Internet led to a new form of digital VMD as implemented in DVRs, which proved to be unsuccessful as a result of poor algorithm designs and the heavy processing required to accommodate outdoor scenarios.
As a result of advances in miniaturisation technology and increased processing power, a new form of VMD was born: Video analytics – a more cost effective, powerful and efficient solution which is also simple to set up and operate.
The four architectures
Four different video analytics architectures can be identified in today’s market: software based; OEM hardware-based; hybrid; and edge devices. Here we list their advantages and disadvantages.
1. Software solutions are comprised of one or more high-end PCs, video capture cards and licensed analytics software.
Pros:
Off the shelf components.
Cons:
Multiple components require heavy integration;
Centralised – relies on analogue infrastructure;
Bandwidth killer;
Complex to install and set up;
Multiple vendors to deal with.
2. OEM hardware solutions (Licensed analytics) are tailored for a DSP platform, enabling third party manufacturers to build video analytics into their products.
Pros:
Hardware based – single component analytics (in theory)
Cons:
Performance issues – does not perform as well as pure software or true edge devices;
Ambiguous point of contact for purchasing and support;
Complex to install and set up;
May require additional components.
3. Hybrid solutions endeavour to benefit from the best of both worlds. Hardware-based and software-based solutions.
Pros:
More capacity/processing channels on a single server.
Cons:
Centralised and edge processing;
Complex to install and set up;
Intricate business model geared towards integration with other manufacturers to allow code injection at the edge device, i.e. camera or video encoder;
Complex pricing;
Suitable only for IP architecture.
4. Edge devices, or Intelligent video appliances, enable existing surveillance systems to become proactive and offer greater ease of installation and a broader scope of utilisation.
Pros:
Simple solution – single component; installs in minutes; minimal maintenance;
Improved performance (native hardware design, automated sensitivity, balanced accuracy and reliability, better survivability);
Easy to do business with – single purchasing and support interfaces, simple pricing;
Cost effective;
Available in various packages as integrated components: video encoders; wide dynamic range cameras; mega pixel electronic PTZ cameras; and mechanical PTZ cameras.
Cons:
Proprietary
PoD and false alarms: finding the balance
A decisive factor in choosing an effective video analytics system is its capability to maintain a low rate of false alarms while allowing a high rate of probability of detection (PoD).
A system that generates frequent false alarms by reacting to extraneous stimuli is ineffective and counteracts the main purpose of video analytics, which is to deliver only relevant information.
On the other hand, the system’s PoD needs to be high enough to ensure that relevant security events are not missed.
The following parameters allow users to achieve a fine balance between PoD and false alarms:
1. Defining the detection zone is necessary in order to reflect the area of interest, since the camera has a large field of view (FoV) and only specific areas are relevant for detection purposes.
There are three types of detection zones that can be defined by most systems: active (alarm zone); inactive (pre-alarm zone); and passive.
The active detection zone (or alarm zone) refers to the areas where an alert is required when a certain event occurs.
The inactive detection zone refers to areas within the camera’s FoV but where activities are ignored.
The passive zone (pre alarm zone), is defined as a zone where the system is aware of activities but will deliver an alarm only when an activity passes from that zone to an active zone. For instance, it could be a fence separating a public and private area. In this scenario, the public area would be defined as the passive or pre-alarm zone and the private area as the alarm zone.
Activities in the public area are ignored unless an intruder attempts to climb the fence to reach the active zone, which would instantly trigger an alarm. This allows harmless activities occurring in proximity of the private area to be ignored, while allowing a higher PoD, faster reaction time and fewer false alarms.
2. Detection rules define the types of detection that are of interest, such as intruders, tripwires, unattended baggage, loitering and stopped vehicles. Detection rules are important in order to reduce false alarms. For example, one may wish to identify people entering a location from an unauthorised access point such as an exit, while allowing free passage to people exiting that location. In this case, the detection rule would define the sense of movement that is allowed.
3. Depth setup enables a computer to translate 2D images into 3D viewing. The human brain has the innate capability of understanding the size of an object in relation to its distance. Depth setup gives a computer similar capability, enabling it to distinguish between an airplane in the background and a fly on the lens.
Other key elements that affect the reliability of video analytics in detecting security events and ignoring irrelevant information include lighting conditions (minimum required light intensity within the detection zone at night); defining the expected speed and size of an object; and selecting the appropriate camera lens for the desired coverage area.
Keeping it Simple
A key challenge that system integrators face when dealing with video analytics is the customer’s desire for a system that can be rapidly installed – thus minimising surveillance downtime due to installation – and it being simple to operate – minimising the learning curve involved.
As mentioned in the architecture section above, a DSP-based edge device offers a stand- alone solution similar to a network switch, where the device simply needs to be hooked up and configured through the network.
This type of architecture allows system integrators and their customers to easily upgrade existing surveillance systems with a plug & play device.
When upgrading an existing basic camera-to-DVR architecture, video analytics is easily introduced without making any changes to the current setup or the system’s operation by security guards.
Adding video analytics to a CCTV system enables the automatic detection of security breaches as defined by the end user, with a screen overlay of the detection displayed on the Matrix/DVR.
It is also possible to use the sensor’s dry contacts to trigger recording and event tagging within the DVR.
To further simplify the intelligent video sensor, an HTML-based setup allows access to the video analytics component via a local network and quickly deploys the built-in intelligence to propagate through the CCTV system.
For those installations where a distributed IP architecture is preferable, Power over Ethernet (PoE) support built into the edge device allows savings on the cost of cabling and conduits and simplifies installation and maintenance.
Basics of reliability: Recognition, certification and the installer base
In tandem with the increased interest in video analytics over the past five years, many new companies have emerged in the market. With varying levels of performance and no unified standard, the user would be well-advised to find out how well a system performs and seek installation references.
The military represents the perfect test bed for security products. With diverse locations and challenges such as rain, snow, wind, darkness, animals, cloud shadows, slow-moving or camouflaged intruders and far distance detection, the army is an ideal organisation to test and approve a new technology and provide feedback for improvement.
Many technologies available to the public today originated in military labs and testing grounds.
A video analytics system that has passed the harshest tests in the most difficult terrains is obviously suitable for high risk sites.
The criteria for success in a military test include a low false alarm rate; high PoD; ability to perform well over long periods of time (sometimes measured in years); rapid deployment; and ease of integration.
These factors, along with a bullet-proof design, are all necessary for off the shelf purchasing and installing.
Last, but probably most important, are references from actual system installations around the world.
The more sites the system has been installed at, the more diversified is the manufacturer’s experience in dealing with myriad detection scenarios and false alarms reduction.
